2_MSA.ipynb

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   "id": "35efae9e-83ff-4d97-8b59-92a35750be7b",
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     "text": [
      "/Users/julijamaldutyte/anaconda3/lib/python3.11/site-packages/Bio/SeqRecord.py:355: BiopythonDeprecationWarning: Using a string as the sequence is deprecated and will raise a TypeError in future. It has been converted to a Seq object.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Import all libraries needed for this notebook\n",
    "from Bio import SeqIO\n",
    "from Bio.SeqRecord import SeqRecord\n",
    "import re\n",
    "from Bio import AlignIO\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.display import display\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "from scipy.stats import spearmanr\n",
    "from sklearn.linear_model import RidgeCV\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler, PolynomialFeatures\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Convert .a3m to .fasta\n",
    "# Preprocess the a3m file to remove insertions (lowercase letters)\n",
    "def preprocess_a3m(a3m_file, processed_file):\n",
    "    with open(a3m_file, \"r\") as infile, open(processed_file, \"w\") as outfile:\n",
    "        for record in SeqIO.parse(infile, \"fasta\"):\n",
    "            sequence = str(record.seq)\n",
    "            processed_sequence = re.sub(r'[a-z]', '', sequence)\n",
    "            record.seq = processed_sequence\n",
    "            SeqIO.write(record, outfile, \"fasta\")\n",
    "\n",
    "# Convert the A3M file to FASTA format after preprocessing\n",
    "def convert_a3m_to_fasta(a3m_file, fasta_file):\n",
    "    preprocessed_file = \"preprocessed_a3m.fasta\"\n",
    "    preprocess_a3m(a3m_file, preprocessed_file)\n",
    "    \n",
    "    # Read the preprocessed file and write to the final FASTA file\n",
    "    alignment = list(SeqIO.parse(preprocessed_file, \"fasta\"))\n",
    "    SeqIO.write(alignment, fasta_file, \"fasta\")\n",
    "    \n",
    "convert_a3m_to_fasta(\"ube2i_msa.a3m\", \"ube2i_msa.fasta\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e1ca8b96-6472-4b36-89ad-ca6139da637d",
   "metadata": {},
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   "source": [
    "# Function to read MSA from FASTA file and convert to a frequency matrix\n",
    "def read_msa(msa_file):\n",
    "    alignment = AlignIO.read(msa_file, \"fasta\")\n",
    "    num_sequences = len(alignment)\n",
    "    sequence_length = alignment.get_alignment_length()\n",
    "    amino_acids = \"ARNDCQEGHILKMFPSTWYV\"\n",
    "    aa_index = {aa: i for i, aa in enumerate(amino_acids)}\n",
    "    \n",
    "    frequency_matrix = np.zeros((sequence_length, len(amino_acids)))\n",
    "    \n",
    "    for record in alignment:\n",
    "        for i, aa in enumerate(record.seq):\n",
    "            if aa in aa_index:\n",
    "                frequency_matrix[i, aa_index[aa]] += 1\n",
    "    \n",
    "    frequency_matrix /= num_sequences\n",
    "    return frequency_matrix, amino_acids\n",
    "\n",
    "# Function to calculate the PSSM (position-specific scoring matrix) from the frequency matrix with pseudocounts\n",
    "def calculate_pssm(frequency_matrix, background_freq=None, pseudocount=1e-6):\n",
    "    if background_freq is None:\n",
    "        background_freq = np.ones(frequency_matrix.shape[1]) / frequency_matrix.shape[1]\n",
    "    \n",
    "    # Add pseudocounts to avoid log(0)\n",
    "    frequency_matrix += pseudocount\n",
    "    \n",
    "    pssm = np.log(frequency_matrix / background_freq)\n",
    "    return pssm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dc706915-9e4f-4d80-a061-e73b3a3c9553",
   "metadata": {},
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   "source": [
    "# Function to generate PSSM for a mutant sequence\n",
    "def get_mutant_pssm_features(wild_type_sequence, mutant_sequence, pssm, amino_acids):\n",
    "    aa_index = {aa: i for i, aa in enumerate(amino_acids)}\n",
    "    mutant_pssm = pssm.copy()\n",
    "    for i, (wt_aa, mut_aa) in enumerate(zip(wild_type_sequence, mutant_sequence)):\n",
    "        if wt_aa != mut_aa and mut_aa in aa_index:\n",
    "            mut_aa_index = aa_index[mut_aa]\n",
    "            mutant_pssm[i] = np.zeros(len(amino_acids))  # Reset to zero\n",
    "            mutant_pssm[i, mut_aa_index] = pssm[i, mut_aa_index]\n",
    "    return mutant_pssm.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f96d1612-e4a8-4f04-96e6-f08fb8575f91",
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   "source": [
    "# Read MSA and calculate frequency matrix\n",
    "msa_file = \"ube2i_msa.fasta\"\n",
    "frequency_matrix, amino_acids = read_msa(msa_file)"
   ]
  },
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   "cell_type": "code",
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   "id": "45fa538e-0278-4924-a3c1-16d01aa11db4",
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     "text": [
      "PSSM:\n",
      "[[ -1.67125401  -3.56130343  -3.46899225 ...  -5.38219767  -5.52462951\n",
      "   -3.08353526]\n",
      " [ -0.45685934  -2.8060094   -1.94386486 ...  -5.60425432  -3.34045237\n",
      "   -2.66497433]\n",
      " [ -0.83755843  -2.250873    -1.01621052 ...  -5.8901275   -4.62682558\n",
      "   -2.5768913 ]\n",
      " ...\n",
      " [  1.63089316  -2.60127626  -3.66301068 ...  -6.13937862  -5.09560369\n",
      "   -3.25780547]\n",
      " [ -1.70930245  -3.08353526  -3.44002306 ...  -6.4721476   -6.4721476\n",
      "   -4.45529808]\n",
      " [ -4.3553786   -4.72505425  -5.1467251  ... -10.81977828  -8.03113034\n",
      "   -6.4721476 ]]\n",
      "Shape of PSSM: (158, 20)\n"
     ]
    }
   ],
   "source": [
    "# Calculate background frequencies\n",
    "background_freq = np.ones(len(amino_acids)) / len(amino_acids)\n",
    "\n",
    "# Calculate PSSM\n",
    "pssm = calculate_pssm(frequency_matrix, background_freq)\n",
    "\n",
    "# Print the PSSM\n",
    "print(\"PSSM:\")\n",
    "print(pssm)\n",
    "print(\"Shape of PSSM:\", pssm.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d5a32199-7af8-4112-87c3-1b79bdd0fbd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read mutant sequences\n",
    "mutant_fasta_file = \"ube2i_single_point_mutants.fasta\"  \n",
    "mutant_sequences = list(SeqIO.parse(mutant_fasta_file, \"fasta\"))\n",
    "\n",
    "# Wild-type sequence (the first sequence in the MSA)\n",
    "wild_type_sequence = str(AlignIO.read(msa_file, \"fasta\")[0].seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "efb063db-4ce6-4cad-8284-c7df7331c9c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate PSSMs for all mutants and save them\n",
    "pssm_features = {}\n",
    "for mutant in mutant_sequences:\n",
    "    mutant_sequence = str(mutant.seq)\n",
    "    features = get_mutant_pssm_features(wild_type_sequence, mutant_sequence, pssm, amino_acids)\n",
    "    pssm_features[mutant.id] = features"
   ]
  },
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   "cell_type": "code",
   "execution_count": 8,
   "id": "80215916-5d65-40b3-b9eb-aad8f0d40bde",
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      "Array 'K49H:1.1266822814941406' has shape: (3160,)\n",
      "Array 'L38P:0.6313150525093079' has shape: (3160,)\n",
      "Array 'A131N:1.093359351158142' has shape: (3160,)\n",
      "Array 'H20I:0.4926813244819641' has shape: (3160,)\n",
      "Array 'M39K:0.5758859515190125' has shape: (3160,)\n",
      "Array 'T35C:0.3537033498287201' has shape: (3160,)\n",
      "Array 'K18F:0.5160290598869324' has shape: (3160,)\n",
      "Array 'F64N:0.539947509765625' has shape: (3160,)\n",
      "Array 'D100H:1.1926833391189575' has shape: (3160,)\n",
      "Array 'V142M:1.0818783044815063' has shape: (3160,)\n",
      "Array 'R147M:0.9772766828536987' has shape: (3160,)\n",
      "Array 'W41E:0.6699082851409912' has shape: (3160,)\n",
      "Array 'N40W:0.4901171028614044' has shape: (3160,)\n",
      "Array 'D66C:0.9968680143356323' has shape: (3160,)\n",
      "Array 'R149M:0.7540002465248108' has shape: (3160,)\n",
      "Array 'A129M:0.310947060585022' has shape: (3160,)\n",
      "Array 'F155M:0.4806143343448639' has shape: (3160,)\n",
      "Array 'I45Y:0.5450822710990906' has shape: (3160,)\n",
      "Array 'T35M:0.4506959617137909' has shape: (3160,)\n",
      "Array 'T51I:0.39919379353523254' has shape: (3160,)\n",
      "Array 'P21M:0.532707929611206' has shape: (3160,)\n",
      "Array 'T135M:0.7810076475143433' has shape: (3160,)\n",
      "Array 'V92P:0.6594555377960205' has shape: (3160,)\n",
      "Array 'D102K:0.6218876242637634' has shape: (3160,)\n",
      "Array 'E12F:0.3616458773612976' has shape: (3160,)\n",
      "Array 'L38Y:1.092607021331787' has shape: (3160,)\n",
      "Array 'R13G:0.28892892599105835' has shape: (3160,)\n",
      "Array 'R104H:0.35319849848747253' has shape: (3160,)\n",
      "Array 'T91W:1.1191000938415527' has shape: (3160,)\n",
      "Array 'G56Q:0.4232478141784668' has shape: (3160,)\n",
      "Array 'L81D:0.347574383020401' has shape: (3160,)\n",
      "Array 'S70M:0.82719886302948' has shape: (3160,)\n",
      "Array 'Q117W:0.6389662623405457' has shape: (3160,)\n",
      "Array 'A156Y:0.6504872441291809' has shape: (3160,)\n",
      "Array 'E118F:1.4258090257644653' has shape: (3160,)\n",
      "Array 'L6D:0.604158341884613' has shape: (3160,)\n",
      "Array 'S2Y:0.9596362113952637' has shape: (3160,)\n",
      "Array 'A131D:0.6719408631324768' has shape: (3160,)\n",
      "Array 'G50T:0.4880620837211609' has shape: (3160,)\n",
      "Array 'G115F:0.31137576699256897' has shape: (3160,)\n",
      "Array 'N31F:0.7078090906143188' has shape: (3160,)\n",
      "Array 'K153W:0.5470666885375977' has shape: (3160,)\n",
      "Array 'F22H:0.6850844621658325' has shape: (3160,)\n",
      "Array 'G3H:0.6828582882881165' has shape: (3160,)\n",
      "Array 'I136T:0.7452918291091919' has shape: (3160,)\n",
      "Array 'L94E:0.5530992150306702' has shape: (3160,)\n",
      "Array 'I125Q:0.5703643560409546' has shape: (3160,)\n",
      "Array 'I125H:0.38663119077682495' has shape: (3160,)\n",
      "Array 'E54H:0.6607921719551086' has shape: (3160,)\n",
      "Array 'I96E:0.24575674533843994' has shape: (3160,)\n",
      "Array 'V148M:0.37959393858909607' has shape: (3160,)\n",
      "Array 'S2G:1.5446512699127197' has shape: (3160,)\n",
      "Array 'P32D:1.2454383373260498' has shape: (3160,)\n",
      "Array 'A152M:0.4590231478214264' has shape: (3160,)\n",
      "Array 'G115W:0.30099600553512573' has shape: (3160,)\n",
      "Array 'L114D:0.738929271697998' has shape: (3160,)\n",
      "Array 'F155D:0.6664584875106812' has shape: (3160,)\n",
      "Array 'P46F:0.5002633333206177' has shape: (3160,)\n",
      "Array 'Q111K:1.3947957754135132' has shape: (3160,)\n",
      "Array 'M36E:0.48580819368362427' has shape: (3160,)\n",
      "Array 'L114H:0.9157246947288513' has shape: (3160,)\n",
      "Array 'A131W:0.3035735785961151' has shape: (3160,)\n",
      "Array 'L113M:0.8558136820793152' has shape: (3160,)\n",
      "Array 'K154T:0.900669276714325' has shape: (3160,)\n",
      "Array 'A150W:0.7297060489654541' has shape: (3160,)\n",
      "Array 'A156E:1.0836114883422852' has shape: (3160,)\n",
      "Array 'S95M:0.34200653433799744' has shape: (3160,)\n",
      "Array 'E78M:1.2215769290924072' has shape: (3160,)\n",
      "Array 'I45M:1.028429627418518' has shape: (3160,)\n",
      "Array 'A10D:0.0751531794667244' has shape: (3160,)\n",
      "Array 'L6S:0.6061214804649353' has shape: (3160,)\n",
      "Array 'E132C:0.7206774353981018' has shape: (3160,)\n",
      "Array 'W53E:0.43295854330062866' has shape: (3160,)\n",
      "Array 'E98V:0.6917809247970581' has shape: (3160,)\n",
      "Array 'F155W:0.30836719274520874' has shape: (3160,)\n",
      "Array 'L94W:0.47050705552101135' has shape: (3160,)\n",
      "Array 'K154I:0.6351238489151001' has shape: (3160,)\n",
      "Array 'V86N:0.3005240559577942' has shape: (3160,)\n",
      "Array 'G115H:0.2872841954231262' has shape: (3160,)\n",
      "Array 'K49W:0.6887410283088684' has shape: (3160,)\n",
      "Array 'R104F:0.35715919733047485' has shape: (3160,)\n",
      "Array 'V142W:0.8147928714752197' has shape: (3160,)\n",
      "Array 'A131F:0.3991653323173523' has shape: (3160,)\n",
      "Array 'Q151F:0.4929843842983246' has shape: (3160,)\n",
      "Array 'K153M:1.0547853708267212' has shape: (3160,)\n",
      "Array 'T108D:0.7733508348464966' has shape: (3160,)\n",
      "Array 'T108Y:1.6204793453216553' has shape: (3160,)\n",
      "Array 'P105F:0.3087218999862671' has shape: (3160,)\n",
      "Array 'Q117D:0.5898662209510803' has shape: (3160,)\n",
      "Array 'L120D:0.2323346585035324' has shape: (3160,)\n",
      "Array 'S95E:0.46259036660194397' has shape: (3160,)\n",
      "Array 'P84D:0.5936015844345093' has shape: (3160,)\n",
      "Array 'P80W:1.0980384349822998' has shape: (3160,)\n",
      "Array 'L94F:0.7893462777137756' has shape: (3160,)\n",
      "Array 'L119Y:1.2399452924728394' has shape: (3160,)\n",
      "Array 'T135D:0.6096915006637573' has shape: (3160,)\n",
      "Array 'L97M:0.9232414364814758' has shape: (3160,)\n",
      "Array 'A26Q:0.43533506989479065' has shape: (3160,)\n",
      "Array 'Q117E:0.7277515530586243' has shape: (3160,)\n",
      "Array 'Q11F:0.6281057000160217' has shape: (3160,)\n",
      "Array 'I116M:1.2527676820755005' has shape: (3160,)\n",
      "Array 'K48M:0.8027977347373962' has shape: (3160,)\n",
      "Array 'R104Y:0.3383142352104187' has shape: (3160,)\n",
      "Array 'F58C:0.46281635761260986' has shape: (3160,)\n",
      "Array 'S70Y:0.9242988228797913' has shape: (3160,)\n",
      "Array 'Y144F:0.45913511514663696' has shape: (3160,)\n",
      "Array 'I4E:0.9596362113952637' has shape: (3160,)\n",
      "Array 'P84N:1.0589487552642822' has shape: (3160,)\n",
      "Array 'A106W:0.3557482063770294' has shape: (3160,)\n",
      "Array 'K110N:0.7082229256629944' has shape: (3160,)\n",
      "Array 'Q11I:1.112227439880371' has shape: (3160,)\n",
      "Array 'M39E:1.147406816482544' has shape: (3160,)\n",
      "Array 'E143Q:0.8833827376365662' has shape: (3160,)\n",
      "Array 'A10N:1.1485626697540283' has shape: (3160,)\n",
      "Array 'P128W:0.44971001148223877' has shape: (3160,)\n",
      "Array 'I4G:0.5799577236175537' has shape: (3160,)\n",
      "Array 'A152K:0.37615421414375305' has shape: (3160,)\n",
      "Array 'P157C:0.8277536034584045' has shape: (3160,)\n",
      "Array 'A26W:0.7287948727607727' has shape: (3160,)\n",
      "Array 'D33W:0.6203053593635559' has shape: (3160,)\n",
      "Array 'H20F:0.4398317337036133' has shape: (3160,)\n",
      "Array 'F22A:0.4829408526420593' has shape: (3160,)\n",
      "Array 'W41K:0.503174901008606' has shape: (3160,)\n",
      "Array 'A44W:0.7102508544921875' has shape: (3160,)\n",
      "Array 'Q151W:0.3566814363002777' has shape: (3160,)\n",
      "Array 'N140M:0.4732598662376404' has shape: (3160,)\n",
      "Array 'Q11Y:1.0609891414642334' has shape: (3160,)\n",
      "Array 'G34H:1.2484878301620483' has shape: (3160,)\n",
      "Array 'K154Q:1.0258581638336182' has shape: (3160,)\n",
      "Array 'D102H:1.158374309539795' has shape: (3160,)\n",
      "Array 'I125Y:0.3742572069168091' has shape: (3160,)\n",
      "Array 'E99W:0.6467829346656799' has shape: (3160,)\n",
      "Array 'V86K:0.6355509757995605' has shape: (3160,)\n",
      "Array 'A15C:0.9973273873329163' has shape: (3160,)\n",
      "Array 'P32K:1.252968192100525' has shape: (3160,)\n",
      "Array 'E42H:0.6144184470176697' has shape: (3160,)\n",
      "Array 'K154F:0.43298593163490295' has shape: (3160,)\n",
      "Array 'P79C:1.0255531072616577' has shape: (3160,)\n",
      "Array 'P157W:1.0549309253692627' has shape: (3160,)\n",
      "Array 'F22Q:0.4156077802181244' has shape: (3160,)\n",
      "Array 'E118W:0.6735261678695679' has shape: (3160,)\n",
      "Array 'G56M:0.5874699354171753' has shape: (3160,)\n",
      "Array 'T51D:0.3885917365550995' has shape: (3160,)\n",
      "Array 'P157K:1.3343148231506348' has shape: (3160,)\n",
      "Array 'D100M:2.0578625202178955' has shape: (3160,)\n",
      "Array 'K14Y:0.5284250378608704' has shape: (3160,)\n",
      "Array 'Y134M:0.7629243731498718' has shape: (3160,)\n",
      "Array 'R17M:0.39811238646507263' has shape: (3160,)\n",
      "Array 'P157E:0.5164877772331238' has shape: (3160,)\n",
      "Array 'T29Q:0.5630186796188354' has shape: (3160,)\n",
      "Array 'R61F:0.8463540077209473' has shape: (3160,)\n",
      "Array 'L114C:1.0740042924880981' has shape: (3160,)\n",
      "Array 'L57K:0.7592399716377258' has shape: (3160,)\n",
      "Array 'R61M:0.7994633913040161' has shape: (3160,)\n",
      "Array 'G47K:0.38884326815605164' has shape: (3160,)\n",
      "Array 'F24G:0.5884245038032532' has shape: (3160,)\n",
      "Array 'P123C:0.4482417106628418' has shape: (3160,)\n",
      "Array 'N40M:0.9278294444084167' has shape: (3160,)\n",
      "Array 'L57H:1.3829835653305054' has shape: (3160,)\n",
      "Array 'T51Q:1.143314242362976' has shape: (3160,)\n",
      "Array 'G55P:0.7807678580284119' has shape: (3160,)\n",
      "Array 'D33Y:0.4133332669734955' has shape: (3160,)\n",
      "Array 'R104C:0.8799723386764526' has shape: (3160,)\n",
      "Array 'E98H:1.3444750308990479' has shape: (3160,)\n",
      "Array 'G90C:0.5740311145782471' has shape: (3160,)\n",
      "Array 'P79K:1.1858272552490234' has shape: (3160,)\n",
      "Array 'D19M:0.6867388486862183' has shape: (3160,)\n",
      "Array 'M39Q:0.3783518671989441' has shape: (3160,)\n",
      "Array 'C93Q:0.5119748711585999' has shape: (3160,)\n",
      "Array 'I96N:0.37199288606643677' has shape: (3160,)\n",
      "Array 'P21N:0.5811270475387573' has shape: (3160,)\n",
      "Array 'A150C:0.823256254196167' has shape: (3160,)\n",
      "Array 'A10H:0.4721480906009674' has shape: (3160,)\n",
      "Array 'T29Y:1.352437138557434' has shape: (3160,)\n",
      "Array 'L60D:0.38673654198646545' has shape: (3160,)\n",
      "Array 'V25W:0.6132054924964905' has shape: (3160,)\n",
      "Array 'C93E:0.5286880135536194' has shape: (3160,)\n",
      "Array 'P88Y:0.5752726793289185' has shape: (3160,)\n",
      "Array 'E42I:0.3843560814857483' has shape: (3160,)\n",
      "Array 'P128C:0.6611109375953674' has shape: (3160,)\n",
      "Array 'T108M:1.1712205410003662' has shape: (3160,)\n",
      "Array 'G115N:0.31750380992889404' has shape: (3160,)\n",
      "Array 'L81N:0.2323083132505417' has shape: (3160,)\n",
      "Array 'S89F:0.9625224471092224' has shape: (3160,)\n",
      "Array 'R147Y:0.643025279045105' has shape: (3160,)\n",
      "Array 'Q151N:0.5975987315177917' has shape: (3160,)\n",
      "Array 'E98Q:0.3809596300125122' has shape: (3160,)\n",
      "Array 'V142Q:0.9417978525161743' has shape: (3160,)\n",
      "Array 'L120M:0.8768439888954163' has shape: (3160,)\n",
      "Array 'G50Q:0.8114463090896606' has shape: (3160,)\n",
      "Array 'Q139N:0.8578505516052246' has shape: (3160,)\n",
      "Array 'P84W:0.4223858714103699' has shape: (3160,)\n",
      "Array 'R149W:0.7841180562973022' has shape: (3160,)\n",
      "Array 'N140W:0.34641847014427185' has shape: (3160,)\n",
      "Array 'P32E:0.8242914080619812' has shape: (3160,)\n",
      "Array 'K59M:0.6112364530563354' has shape: (3160,)\n",
      "Array 'F82W:0.7814753651618958' has shape: (3160,)\n",
      "Array 'R147H:0.7015586495399475' has shape: (3160,)\n",
      "Array 'E145M:0.877712070941925' has shape: (3160,)\n",
      "Array 'N85Q:0.3609381318092346' has shape: (3160,)\n",
      "Array 'L119K:0.36521297693252563' has shape: (3160,)\n",
      "Array 'M36Q:0.4120548367500305' has shape: (3160,)\n",
      "Array 'R13Y:0.38310542702674866' has shape: (3160,)\n",
      "Array 'Q11W:0.8915714621543884' has shape: (3160,)\n",
      "Array 'N124F:0.6843185424804688' has shape: (3160,)\n",
      "Array 'L94D:0.5889070630073547' has shape: (3160,)\n",
      "Array 'R61H:0.8086597919464111' has shape: (3160,)\n",
      "Array 'F22M:0.34144583344459534' has shape: (3160,)\n",
      "Array 'E98I:2.0604984760284424' has shape: (3160,)\n",
      "Array 'F22D:0.5539947152137756' has shape: (3160,)\n",
      "Array 'L9C:0.7428427934646606' has shape: (3160,)\n",
      "Array 'L9K:0.6582711935043335' has shape: (3160,)\n",
      "Array 'F58M:0.5656247735023499' has shape: (3160,)\n",
      "Array 'L60C:1.1304643154144287' has shape: (3160,)\n",
      "Array 'E42K:0.965642511844635' has shape: (3160,)\n",
      "Array 'Q130Y:0.5516899824142456' has shape: (3160,)\n",
      "Array 'T51Y:0.43242308497428894' has shape: (3160,)\n",
      "Array 'W16Q:0.44063854217529297' has shape: (3160,)\n",
      "Array 'P72M:0.23545676469802856' has shape: (3160,)\n",
      "Array 'H83M:0.3666467070579529' has shape: (3160,)\n",
      "Array 'P21D:0.44346553087234497' has shape: (3160,)\n",
      "Array 'G90H:0.30997613072395325' has shape: (3160,)\n",
      "Array 'P52Y:0.9028836488723755' has shape: (3160,)\n",
      "Array 'S70K:0.572797417640686' has shape: (3160,)\n",
      "Array 'N31M:1.0085095167160034' has shape: (3160,)\n",
      "Array 'F22C:0.35075581073760986' has shape: (3160,)\n",
      "Array 'S2N:0.638947069644928' has shape: (3160,)\n",
      "Array 'K30C:0.4355754554271698' has shape: (3160,)\n",
      "Array 'W103M:0.4886203408241272' has shape: (3160,)\n",
      "Array 'P28W:0.2589836120605469' has shape: (3160,)\n",
      "Array 'V92Y:0.4208049178123474' has shape: (3160,)\n",
      "Array 'L63C:0.5420646071434021' has shape: (3160,)\n",
      "Array 'Q130D:0.3870650827884674' has shape: (3160,)\n",
      "Array 'A152W:0.44388049840927124' has shape: (3160,)\n",
      "Array 'I45D:0.437007337808609' has shape: (3160,)\n",
      "Array 'R141H:1.0854507684707642' has shape: (3160,)\n",
      "Array 'A15W:0.6964787244796753' has shape: (3160,)\n",
      "Array 'P28F:0.5048689246177673' has shape: (3160,)\n",
      "Array 'I112Y:0.28946661949157715' has shape: (3160,)\n",
      "Array 'P32W:0.9419150352478027' has shape: (3160,)\n",
      "Array 'Q139W:0.7723677754402161' has shape: (3160,)\n"
     ]
    }
   ],
   "source": [
    "for key in pssm_features.keys():\n",
    "    print(f\"Array '{key}' has shape: {pssm_features[key].shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a96e3b6d-ce8e-4dab-945b-270737fccd23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         id  feature_1  feature_2  feature_3  feature_4  \\\n",
      "0   K30D:0.5080366134643555  -1.671254  -3.561303  -3.468992  -4.429367   \n",
      "1   D127S:0.619499683380127  -1.671254  -3.561303  -3.468992  -4.429367   \n",
      "2  L114Y:0.9718983173370361  -1.671254  -3.561303  -3.468992  -4.429367   \n",
      "3   H83F:0.5161952972412109  -1.671254  -3.561303  -3.468992  -4.429367   \n",
      "4   R61G:0.7325457334518433  -1.671254  -3.561303  -3.468992  -4.429367   \n",
      "\n",
      "   feature_5  feature_6  feature_7  feature_8  feature_9  ...  feature_3151  \\\n",
      "0  -4.873114  -3.711762  -4.455298  -2.982225  -4.796346  ...     -5.890127   \n",
      "1  -4.873114  -3.711762  -4.455298  -2.982225  -4.796346  ...     -5.890127   \n",
      "2  -4.873114  -3.711762  -4.455298  -2.982225  -4.796346  ...     -5.890127   \n",
      "3  -4.873114  -3.711762  -4.455298  -2.982225  -4.796346  ...     -5.890127   \n",
      "4  -4.873114  -3.711762  -4.455298  -2.982225  -4.796346  ...     -5.890127   \n",
      "\n",
      "   feature_3152  feature_3153  feature_3154  feature_3155  feature_3156  \\\n",
      "0     -4.796346      -8.03113     -7.369218     -6.139379     -2.630498   \n",
      "1     -4.796346      -8.03113     -7.369218     -6.139379     -2.630498   \n",
      "2     -4.796346      -8.03113     -7.369218     -6.139379     -2.630498   \n",
      "3     -4.796346      -8.03113     -7.369218     -6.139379     -2.630498   \n",
      "4     -4.796346      -8.03113     -7.369218     -6.139379     -2.630498   \n",
      "\n",
      "   feature_3157  feature_3158  feature_3159  feature_3160  \n",
      "0      -5.52463    -10.819778      -8.03113     -6.472148  \n",
      "1      -5.52463    -10.819778      -8.03113     -6.472148  \n",
      "2      -5.52463    -10.819778      -8.03113     -6.472148  \n",
      "3      -5.52463    -10.819778      -8.03113     -6.472148  \n",
      "4      -5.52463    -10.819778      -8.03113     -6.472148  \n",
      "\n",
      "[5 rows x 3161 columns]\n"
     ]
    }
   ],
   "source": [
    "# Convert the dictionary to a data frame\n",
    "mutant_pssm = pd.DataFrame.from_dict(pssm_features, orient='index')\n",
    "\n",
    "# Add column names\n",
    "num_features = mutant_pssm.shape[1]\n",
    "mutant_pssm.columns = [f\"feature_{i+1}\" for i in range(num_features)]\n",
    "\n",
    "# Reset the index to convert the index into a column\n",
    "mutant_pssm.reset_index(inplace=True)\n",
    "\n",
    "# Rename the index column to 'id'\n",
    "mutant_pssm.rename(columns={'index': 'id'}, inplace=True)\n",
    "\n",
    "print(mutant_pssm.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d9eef6bd-22e7-4cec-93d1-7927666ab091",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bin edges (boundaries): [0.         0.34742916 0.41501102 0.49650659 0.62802055 0.77240558\n",
      " 0.9084807  1.10469232 4.10751009]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>fitness</th>\n",
       "      <th>fitness_group</th>\n",
       "      <th>feature_1</th>\n",
       "      <th>feature_2</th>\n",
       "      <th>feature_3</th>\n",
       "      <th>feature_4</th>\n",
       "      <th>feature_5</th>\n",
       "      <th>feature_6</th>\n",
       "      <th>feature_7</th>\n",
       "      <th>...</th>\n",
       "      <th>feature_3151</th>\n",
       "      <th>feature_3152</th>\n",
       "      <th>feature_3153</th>\n",
       "      <th>feature_3154</th>\n",
       "      <th>feature_3155</th>\n",
       "      <th>feature_3156</th>\n",
       "      <th>feature_3157</th>\n",
       "      <th>feature_3158</th>\n",
       "      <th>feature_3159</th>\n",
       "      <th>feature_3160</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>K30D:0.5080366134643555</td>\n",
       "      <td>0.508037</td>\n",
       "      <td>3</td>\n",
       "      <td>-1.671254</td>\n",
       "      <td>-3.561303</td>\n",
       "      <td>-3.468992</td>\n",
       "      <td>-4.429367</td>\n",
       "      <td>-4.873114</td>\n",
       "      <td>-3.711762</td>\n",
       "      <td>-4.455298</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.890127</td>\n",
       "      <td>-4.796346</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-7.369218</td>\n",
       "      <td>-6.139379</td>\n",
       "      <td>-2.630498</td>\n",
       "      <td>-5.52463</td>\n",
       "      <td>-10.819778</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-6.472148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>D127S:0.619499683380127</td>\n",
       "      <td>0.619500</td>\n",
       "      <td>3</td>\n",
       "      <td>-1.671254</td>\n",
       "      <td>-3.561303</td>\n",
       "      <td>-3.468992</td>\n",
       "      <td>-4.429367</td>\n",
       "      <td>-4.873114</td>\n",
       "      <td>-3.711762</td>\n",
       "      <td>-4.455298</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.890127</td>\n",
       "      <td>-4.796346</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-7.369218</td>\n",
       "      <td>-6.139379</td>\n",
       "      <td>-2.630498</td>\n",
       "      <td>-5.52463</td>\n",
       "      <td>-10.819778</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-6.472148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>L114Y:0.9718983173370361</td>\n",
       "      <td>0.971898</td>\n",
       "      <td>6</td>\n",
       "      <td>-1.671254</td>\n",
       "      <td>-3.561303</td>\n",
       "      <td>-3.468992</td>\n",
       "      <td>-4.429367</td>\n",
       "      <td>-4.873114</td>\n",
       "      <td>-3.711762</td>\n",
       "      <td>-4.455298</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.890127</td>\n",
       "      <td>-4.796346</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-7.369218</td>\n",
       "      <td>-6.139379</td>\n",
       "      <td>-2.630498</td>\n",
       "      <td>-5.52463</td>\n",
       "      <td>-10.819778</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-6.472148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>H83F:0.5161952972412109</td>\n",
       "      <td>0.516195</td>\n",
       "      <td>3</td>\n",
       "      <td>-1.671254</td>\n",
       "      <td>-3.561303</td>\n",
       "      <td>-3.468992</td>\n",
       "      <td>-4.429367</td>\n",
       "      <td>-4.873114</td>\n",
       "      <td>-3.711762</td>\n",
       "      <td>-4.455298</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.890127</td>\n",
       "      <td>-4.796346</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-7.369218</td>\n",
       "      <td>-6.139379</td>\n",
       "      <td>-2.630498</td>\n",
       "      <td>-5.52463</td>\n",
       "      <td>-10.819778</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-6.472148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R61G:0.7325457334518433</td>\n",
       "      <td>0.732546</td>\n",
       "      <td>4</td>\n",
       "      <td>-1.671254</td>\n",
       "      <td>-3.561303</td>\n",
       "      <td>-3.468992</td>\n",
       "      <td>-4.429367</td>\n",
       "      <td>-4.873114</td>\n",
       "      <td>-3.711762</td>\n",
       "      <td>-4.455298</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.890127</td>\n",
       "      <td>-4.796346</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-7.369218</td>\n",
       "      <td>-6.139379</td>\n",
       "      <td>-2.630498</td>\n",
       "      <td>-5.52463</td>\n",
       "      <td>-10.819778</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-6.472148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2849</th>\n",
       "      <td>A15W:0.6964787244796753</td>\n",
       "      <td>0.696479</td>\n",
       "      <td>4</td>\n",
       "      <td>-1.671254</td>\n",
       "      <td>-3.561303</td>\n",
       "      <td>-3.468992</td>\n",
       "      <td>-4.429367</td>\n",
       "      <td>-4.873114</td>\n",
       "      <td>-3.711762</td>\n",
       "      <td>-4.455298</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.890127</td>\n",
       "      <td>-4.796346</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-7.369218</td>\n",
       "      <td>-6.139379</td>\n",
       "      <td>-2.630498</td>\n",
       "      <td>-5.52463</td>\n",
       "      <td>-10.819778</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-6.472148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2850</th>\n",
       "      <td>P28F:0.5048689246177673</td>\n",
       "      <td>0.504869</td>\n",
       "      <td>3</td>\n",
       "      <td>-1.671254</td>\n",
       "      <td>-3.561303</td>\n",
       "      <td>-3.468992</td>\n",
       "      <td>-4.429367</td>\n",
       "      <td>-4.873114</td>\n",
       "      <td>-3.711762</td>\n",
       "      <td>-4.455298</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.890127</td>\n",
       "      <td>-4.796346</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-7.369218</td>\n",
       "      <td>-6.139379</td>\n",
       "      <td>-2.630498</td>\n",
       "      <td>-5.52463</td>\n",
       "      <td>-10.819778</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-6.472148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2851</th>\n",
       "      <td>I112Y:0.28946661949157715</td>\n",
       "      <td>0.289467</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.671254</td>\n",
       "      <td>-3.561303</td>\n",
       "      <td>-3.468992</td>\n",
       "      <td>-4.429367</td>\n",
       "      <td>-4.873114</td>\n",
       "      <td>-3.711762</td>\n",
       "      <td>-4.455298</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.890127</td>\n",
       "      <td>-4.796346</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-7.369218</td>\n",
       "      <td>-6.139379</td>\n",
       "      <td>-2.630498</td>\n",
       "      <td>-5.52463</td>\n",
       "      <td>-10.819778</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-6.472148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2852</th>\n",
       "      <td>P32W:0.9419150352478027</td>\n",
       "      <td>0.941915</td>\n",
       "      <td>6</td>\n",
       "      <td>-1.671254</td>\n",
       "      <td>-3.561303</td>\n",
       "      <td>-3.468992</td>\n",
       "      <td>-4.429367</td>\n",
       "      <td>-4.873114</td>\n",
       "      <td>-3.711762</td>\n",
       "      <td>-4.455298</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.890127</td>\n",
       "      <td>-4.796346</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-7.369218</td>\n",
       "      <td>-6.139379</td>\n",
       "      <td>-2.630498</td>\n",
       "      <td>-5.52463</td>\n",
       "      <td>-10.819778</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-6.472148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2853</th>\n",
       "      <td>Q139W:0.7723677754402161</td>\n",
       "      <td>0.772368</td>\n",
       "      <td>4</td>\n",
       "      <td>-1.671254</td>\n",
       "      <td>-3.561303</td>\n",
       "      <td>-3.468992</td>\n",
       "      <td>-4.429367</td>\n",
       "      <td>-4.873114</td>\n",
       "      <td>-3.711762</td>\n",
       "      <td>-4.455298</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.890127</td>\n",
       "      <td>-4.796346</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-7.369218</td>\n",
       "      <td>-6.139379</td>\n",
       "      <td>-2.630498</td>\n",
       "      <td>-5.52463</td>\n",
       "      <td>-10.819778</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-6.472148</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2854 rows × 3163 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             id   fitness  fitness_group  feature_1  \\\n",
       "0       K30D:0.5080366134643555  0.508037              3  -1.671254   \n",
       "1       D127S:0.619499683380127  0.619500              3  -1.671254   \n",
       "2      L114Y:0.9718983173370361  0.971898              6  -1.671254   \n",
       "3       H83F:0.5161952972412109  0.516195              3  -1.671254   \n",
       "4       R61G:0.7325457334518433  0.732546              4  -1.671254   \n",
       "...                         ...       ...            ...        ...   \n",
       "2849    A15W:0.6964787244796753  0.696479              4  -1.671254   \n",
       "2850    P28F:0.5048689246177673  0.504869              3  -1.671254   \n",
       "2851  I112Y:0.28946661949157715  0.289467              0  -1.671254   \n",
       "2852    P32W:0.9419150352478027  0.941915              6  -1.671254   \n",
       "2853   Q139W:0.7723677754402161  0.772368              4  -1.671254   \n",
       "\n",
       "      feature_2  feature_3  feature_4  feature_5  feature_6  feature_7  ...  \\\n",
       "0     -3.561303  -3.468992  -4.429367  -4.873114  -3.711762  -4.455298  ...   \n",
       "1     -3.561303  -3.468992  -4.429367  -4.873114  -3.711762  -4.455298  ...   \n",
       "2     -3.561303  -3.468992  -4.429367  -4.873114  -3.711762  -4.455298  ...   \n",
       "3     -3.561303  -3.468992  -4.429367  -4.873114  -3.711762  -4.455298  ...   \n",
       "4     -3.561303  -3.468992  -4.429367  -4.873114  -3.711762  -4.455298  ...   \n",
       "...         ...        ...        ...        ...        ...        ...  ...   \n",
       "2849  -3.561303  -3.468992  -4.429367  -4.873114  -3.711762  -4.455298  ...   \n",
       "2850  -3.561303  -3.468992  -4.429367  -4.873114  -3.711762  -4.455298  ...   \n",
       "2851  -3.561303  -3.468992  -4.429367  -4.873114  -3.711762  -4.455298  ...   \n",
       "2852  -3.561303  -3.468992  -4.429367  -4.873114  -3.711762  -4.455298  ...   \n",
       "2853  -3.561303  -3.468992  -4.429367  -4.873114  -3.711762  -4.455298  ...   \n",
       "\n",
       "      feature_3151  feature_3152  feature_3153  feature_3154  feature_3155  \\\n",
       "0        -5.890127     -4.796346      -8.03113     -7.369218     -6.139379   \n",
       "1        -5.890127     -4.796346      -8.03113     -7.369218     -6.139379   \n",
       "2        -5.890127     -4.796346      -8.03113     -7.369218     -6.139379   \n",
       "3        -5.890127     -4.796346      -8.03113     -7.369218     -6.139379   \n",
       "4        -5.890127     -4.796346      -8.03113     -7.369218     -6.139379   \n",
       "...            ...           ...           ...           ...           ...   \n",
       "2849     -5.890127     -4.796346      -8.03113     -7.369218     -6.139379   \n",
       "2850     -5.890127     -4.796346      -8.03113     -7.369218     -6.139379   \n",
       "2851     -5.890127     -4.796346      -8.03113     -7.369218     -6.139379   \n",
       "2852     -5.890127     -4.796346      -8.03113     -7.369218     -6.139379   \n",
       "2853     -5.890127     -4.796346      -8.03113     -7.369218     -6.139379   \n",
       "\n",
       "      feature_3156  feature_3157  feature_3158  feature_3159  feature_3160  \n",
       "0        -2.630498      -5.52463    -10.819778      -8.03113     -6.472148  \n",
       "1        -2.630498      -5.52463    -10.819778      -8.03113     -6.472148  \n",
       "2        -2.630498      -5.52463    -10.819778      -8.03113     -6.472148  \n",
       "3        -2.630498      -5.52463    -10.819778      -8.03113     -6.472148  \n",
       "4        -2.630498      -5.52463    -10.819778      -8.03113     -6.472148  \n",
       "...            ...           ...           ...           ...           ...  \n",
       "2849     -2.630498      -5.52463    -10.819778      -8.03113     -6.472148  \n",
       "2850     -2.630498      -5.52463    -10.819778      -8.03113     -6.472148  \n",
       "2851     -2.630498      -5.52463    -10.819778      -8.03113     -6.472148  \n",
       "2852     -2.630498      -5.52463    -10.819778      -8.03113     -6.472148  \n",
       "2853     -2.630498      -5.52463    -10.819778      -8.03113     -6.472148  \n",
       "\n",
       "[2854 rows x 3163 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### To see how much info PSSM contributes, import features.csv, use fitness score from there as target and train on PSSM marix only\n",
    "# Import features.csv\n",
    "\n",
    "features = pd.read_csv(\"features.csv\")\n",
    "features[['mut_id', 'fitness']] = features['id'].str.split(':', expand=True)\n",
    "features = features[['id', 'mut_id', 'fitness'] + [col for col in features.columns if col not in ['id', 'mut_id', 'fitness']]]\n",
    "features['fitness'] = pd.to_numeric(features['fitness'])\n",
    "\n",
    "# Bin fitness scores by quantiles\n",
    "quantile_no = 8\n",
    "features['fitness_group'], bins = pd.qcut(features['fitness'], q=quantile_no, labels=False, retbins=True)\n",
    "\n",
    "# Print bin edges\n",
    "print(\"Bin edges (boundaries):\", bins)\n",
    "features_meta = features[['id', 'fitness', 'fitness_group']]\n",
    "#display(features_meta)\n",
    "\n",
    "# Merge some features columns with the mutant_pssm\n",
    "mutant_pssm_fitness = pd.merge(features_meta, mutant_pssm, on='id')\n",
    "display(mutant_pssm_fitness)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ae00065f-738f-4b15-abb5-6ef3d330ccb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>fitness_group</th>\n",
       "      <th>feature_1</th>\n",
       "      <th>feature_2</th>\n",
       "      <th>feature_3</th>\n",
       "      <th>feature_4</th>\n",
       "      <th>feature_5</th>\n",
       "      <th>feature_6</th>\n",
       "      <th>feature_7</th>\n",
       "      <th>feature_8</th>\n",
       "      <th>...</th>\n",
       "      <th>feature_3151</th>\n",
       "      <th>feature_3152</th>\n",
       "      <th>feature_3153</th>\n",
       "      <th>feature_3154</th>\n",
       "      <th>feature_3155</th>\n",
       "      <th>feature_3156</th>\n",
       "      <th>feature_3157</th>\n",
       "      <th>feature_3158</th>\n",
       "      <th>feature_3159</th>\n",
       "      <th>feature_3160</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>743</th>\n",
       "      <td>F77S:0.34993505477905273</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.671254</td>\n",
       "      <td>-3.561303</td>\n",
       "      <td>-3.468992</td>\n",
       "      <td>-4.429367</td>\n",
       "      <td>-4.873114</td>\n",
       "      <td>-3.711762</td>\n",
       "      <td>-4.455298</td>\n",
       "      <td>-2.982225</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.890127</td>\n",
       "      <td>-4.796346</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-7.369218</td>\n",
       "      <td>-6.139379</td>\n",
       "      <td>-2.630498</td>\n",
       "      <td>-5.52463</td>\n",
       "      <td>-10.819778</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-6.472148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1451</th>\n",
       "      <td>P73N:0.37244078516960144</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.671254</td>\n",
       "      <td>-3.561303</td>\n",
       "      <td>-3.468992</td>\n",
       "      <td>-4.429367</td>\n",
       "      <td>-4.873114</td>\n",
       "      <td>-3.711762</td>\n",
       "      <td>-4.455298</td>\n",
       "      <td>-2.982225</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.890127</td>\n",
       "      <td>-4.796346</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-7.369218</td>\n",
       "      <td>-6.139379</td>\n",
       "      <td>-2.630498</td>\n",
       "      <td>-5.52463</td>\n",
       "      <td>-10.819778</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-6.472148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2247</th>\n",
       "      <td>L57Q:0.9007079005241394</td>\n",
       "      <td>5</td>\n",
       "      <td>-1.671254</td>\n",
       "      <td>-3.561303</td>\n",
       "      <td>-3.468992</td>\n",
       "      <td>-4.429367</td>\n",
       "      <td>-4.873114</td>\n",
       "      <td>-3.711762</td>\n",
       "      <td>-4.455298</td>\n",
       "      <td>-2.982225</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.890127</td>\n",
       "      <td>-4.796346</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-7.369218</td>\n",
       "      <td>-6.139379</td>\n",
       "      <td>-2.630498</td>\n",
       "      <td>-5.52463</td>\n",
       "      <td>-10.819778</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-6.472148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2281</th>\n",
       "      <td>D102W:0.8114498257637024</td>\n",
       "      <td>5</td>\n",
       "      <td>-1.671254</td>\n",
       "      <td>-3.561303</td>\n",
       "      <td>-3.468992</td>\n",
       "      <td>-4.429367</td>\n",
       "      <td>-4.873114</td>\n",
       "      <td>-3.711762</td>\n",
       "      <td>-4.455298</td>\n",
       "      <td>-2.982225</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.890127</td>\n",
       "      <td>-4.796346</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-7.369218</td>\n",
       "      <td>-6.139379</td>\n",
       "      <td>-2.630498</td>\n",
       "      <td>-5.52463</td>\n",
       "      <td>-10.819778</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-6.472148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>T29G:0.9752710461616516</td>\n",
       "      <td>6</td>\n",
       "      <td>-1.671254</td>\n",
       "      <td>-3.561303</td>\n",
       "      <td>-3.468992</td>\n",
       "      <td>-4.429367</td>\n",
       "      <td>-4.873114</td>\n",
       "      <td>-3.711762</td>\n",
       "      <td>-4.455298</td>\n",
       "      <td>-2.982225</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.890127</td>\n",
       "      <td>-4.796346</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-7.369218</td>\n",
       "      <td>-6.139379</td>\n",
       "      <td>-2.630498</td>\n",
       "      <td>-5.52463</td>\n",
       "      <td>-10.819778</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-6.472148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025</th>\n",
       "      <td>F77H:0.3387545049190521</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.671254</td>\n",
       "      <td>-3.561303</td>\n",
       "      <td>-3.468992</td>\n",
       "      <td>-4.429367</td>\n",
       "      <td>-4.873114</td>\n",
       "      <td>-3.711762</td>\n",
       "      <td>-4.455298</td>\n",
       "      <td>-2.982225</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.890127</td>\n",
       "      <td>-4.796346</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-7.369218</td>\n",
       "      <td>-6.139379</td>\n",
       "      <td>-2.630498</td>\n",
       "      <td>-5.52463</td>\n",
       "      <td>-10.819778</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-6.472148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2500</th>\n",
       "      <td>L94N:0.44525304436683655</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.671254</td>\n",
       "      <td>-3.561303</td>\n",
       "      <td>-3.468992</td>\n",
       "      <td>-4.429367</td>\n",
       "      <td>-4.873114</td>\n",
       "      <td>-3.711762</td>\n",
       "      <td>-4.455298</td>\n",
       "      <td>-2.982225</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.890127</td>\n",
       "      <td>-4.796346</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-7.369218</td>\n",
       "      <td>-6.139379</td>\n",
       "      <td>-2.630498</td>\n",
       "      <td>-5.52463</td>\n",
       "      <td>-10.819778</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-6.472148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>L38F:0.8292379379272461</td>\n",
       "      <td>5</td>\n",
       "      <td>-1.671254</td>\n",
       "      <td>-3.561303</td>\n",
       "      <td>-3.468992</td>\n",
       "      <td>-4.429367</td>\n",
       "      <td>-4.873114</td>\n",
       "      <td>-3.711762</td>\n",
       "      <td>-4.455298</td>\n",
       "      <td>-2.982225</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.890127</td>\n",
       "      <td>-4.796346</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-7.369218</td>\n",
       "      <td>-6.139379</td>\n",
       "      <td>-2.630498</td>\n",
       "      <td>-5.52463</td>\n",
       "      <td>-10.819778</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-6.472148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1174</th>\n",
       "      <td>A156F:0.8652383685112</td>\n",
       "      <td>5</td>\n",
       "      <td>-1.671254</td>\n",
       "      <td>-3.561303</td>\n",
       "      <td>-3.468992</td>\n",
       "      <td>-4.429367</td>\n",
       "      <td>-4.873114</td>\n",
       "      <td>-3.711762</td>\n",
       "      <td>-4.455298</td>\n",
       "      <td>-2.982225</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.890127</td>\n",
       "      <td>-4.796346</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-7.369218</td>\n",
       "      <td>-6.139379</td>\n",
       "      <td>-2.630498</td>\n",
       "      <td>-5.52463</td>\n",
       "      <td>-10.819778</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-6.472148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2362</th>\n",
       "      <td>W53D:0.4444430470466614</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.671254</td>\n",
       "      <td>-3.561303</td>\n",
       "      <td>-3.468992</td>\n",
       "      <td>-4.429367</td>\n",
       "      <td>-4.873114</td>\n",
       "      <td>-3.711762</td>\n",
       "      <td>-4.455298</td>\n",
       "      <td>-2.982225</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.890127</td>\n",
       "      <td>-4.796346</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-7.369218</td>\n",
       "      <td>-6.139379</td>\n",
       "      <td>-2.630498</td>\n",
       "      <td>-5.52463</td>\n",
       "      <td>-10.819778</td>\n",
       "      <td>-8.03113</td>\n",
       "      <td>-6.472148</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 3162 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            id  fitness_group  feature_1  feature_2  \\\n",
       "743   F77S:0.34993505477905273              1  -1.671254  -3.561303   \n",
       "1451  P73N:0.37244078516960144              1  -1.671254  -3.561303   \n",
       "2247   L57Q:0.9007079005241394              5  -1.671254  -3.561303   \n",
       "2281  D102W:0.8114498257637024              5  -1.671254  -3.561303   \n",
       "544    T29G:0.9752710461616516              6  -1.671254  -3.561303   \n",
       "...                        ...            ...        ...        ...   \n",
       "2025   F77H:0.3387545049190521              0  -1.671254  -3.561303   \n",
       "2500  L94N:0.44525304436683655              2  -1.671254  -3.561303   \n",
       "480    L38F:0.8292379379272461              5  -1.671254  -3.561303   \n",
       "1174     A156F:0.8652383685112              5  -1.671254  -3.561303   \n",
       "2362   W53D:0.4444430470466614              2  -1.671254  -3.561303   \n",
       "\n",
       "      feature_3  feature_4  feature_5  feature_6  feature_7  feature_8  ...  \\\n",
       "743   -3.468992  -4.429367  -4.873114  -3.711762  -4.455298  -2.982225  ...   \n",
       "1451  -3.468992  -4.429367  -4.873114  -3.711762  -4.455298  -2.982225  ...   \n",
       "2247  -3.468992  -4.429367  -4.873114  -3.711762  -4.455298  -2.982225  ...   \n",
       "2281  -3.468992  -4.429367  -4.873114  -3.711762  -4.455298  -2.982225  ...   \n",
       "544   -3.468992  -4.429367  -4.873114  -3.711762  -4.455298  -2.982225  ...   \n",
       "...         ...        ...        ...        ...        ...        ...  ...   \n",
       "2025  -3.468992  -4.429367  -4.873114  -3.711762  -4.455298  -2.982225  ...   \n",
       "2500  -3.468992  -4.429367  -4.873114  -3.711762  -4.455298  -2.982225  ...   \n",
       "480   -3.468992  -4.429367  -4.873114  -3.711762  -4.455298  -2.982225  ...   \n",
       "1174  -3.468992  -4.429367  -4.873114  -3.711762  -4.455298  -2.982225  ...   \n",
       "2362  -3.468992  -4.429367  -4.873114  -3.711762  -4.455298  -2.982225  ...   \n",
       "\n",
       "      feature_3151  feature_3152  feature_3153  feature_3154  feature_3155  \\\n",
       "743      -5.890127     -4.796346      -8.03113     -7.369218     -6.139379   \n",
       "1451     -5.890127     -4.796346      -8.03113     -7.369218     -6.139379   \n",
       "2247     -5.890127     -4.796346      -8.03113     -7.369218     -6.139379   \n",
       "2281     -5.890127     -4.796346      -8.03113     -7.369218     -6.139379   \n",
       "544      -5.890127     -4.796346      -8.03113     -7.369218     -6.139379   \n",
       "...            ...           ...           ...           ...           ...   \n",
       "2025     -5.890127     -4.796346      -8.03113     -7.369218     -6.139379   \n",
       "2500     -5.890127     -4.796346      -8.03113     -7.369218     -6.139379   \n",
       "480      -5.890127     -4.796346      -8.03113     -7.369218     -6.139379   \n",
       "1174     -5.890127     -4.796346      -8.03113     -7.369218     -6.139379   \n",
       "2362     -5.890127     -4.796346      -8.03113     -7.369218     -6.139379   \n",
       "\n",
       "      feature_3156  feature_3157  feature_3158  feature_3159  feature_3160  \n",
       "743      -2.630498      -5.52463    -10.819778      -8.03113     -6.472148  \n",
       "1451     -2.630498      -5.52463    -10.819778      -8.03113     -6.472148  \n",
       "2247     -2.630498      -5.52463    -10.819778      -8.03113     -6.472148  \n",
       "2281     -2.630498      -5.52463    -10.819778      -8.03113     -6.472148  \n",
       "544      -2.630498      -5.52463    -10.819778      -8.03113     -6.472148  \n",
       "...            ...           ...           ...           ...           ...  \n",
       "2025     -2.630498      -5.52463    -10.819778      -8.03113     -6.472148  \n",
       "2500     -2.630498      -5.52463    -10.819778      -8.03113     -6.472148  \n",
       "480      -2.630498      -5.52463    -10.819778      -8.03113     -6.472148  \n",
       "1174     -2.630498      -5.52463    -10.819778      -8.03113     -6.472148  \n",
       "2362     -2.630498      -5.52463    -10.819778      -8.03113     -6.472148  \n",
       "\n",
       "[96 rows x 3162 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 0.15073790199907663\n",
      "R2 score: -0.19556807374214036\n",
      "Mean Absolute Error: 0.2711242831580576\n",
      "Spearman's correlation: 0.28143331396763577\n",
      "Spearman's p-value: 2.224900093406546e-51\n"
     ]
    }
   ],
   "source": [
    "# Train an rfr\n",
    "# Split the data into features (X) and target (y)\n",
    "X = mutant_pssm_fitness.drop(columns=['fitness'])\n",
    "y = mutant_pssm_fitness['fitness']\n",
    "\n",
    "# Use train_test_split with the stratify parameter\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=96, stratify=X['fitness_group'], random_state=42)\n",
    "\n",
    "display(X_train)\n",
    "\n",
    "# Train the rfr\n",
    "rfr = RandomForestRegressor(random_state=42)\n",
    "rfr.fit(X_train.drop(columns=['id', 'fitness_group']), y_train)\n",
    "\n",
    "# Predict the fitness scores for the test set\n",
    "y_pred = rfr.predict(X_test.drop(columns=['id', 'fitness_group']))\n",
    "\n",
    "# Evaluate the model\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(f\"Mean Squared Error: {mse}\")\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "print(f\"R2 score: {r2}\")\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "print(f\"Mean Absolute Error: {mae}\")\n",
    "\n",
    "# Spearman's correl\n",
    "spearman_corr, spearman_p_value = spearmanr(y_test, y_pred)\n",
    "print(f\"Spearman's correlation: {spearman_corr}\")\n",
    "print(f\"Spearman's p-value: {spearman_p_value}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bc14e7cc-c64b-4cf4-b012-922d820acbd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best alpha: 1000.0\n",
      "Training Score: 0.706473494076256\n",
      "Test Score: 0.07754762165467843\n",
      "Mean Squared Error: 0.11630331995283957\n",
      "R2 score: 0.07754762165467843\n",
      "Mean Absolute Error: 0.2569082361193148\n",
      "Spearman's correlation: 0.3499756209869372\n",
      "Spearman's p-value: 2.7719082640432305e-80\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Ridge regression \n",
    "\n",
    "# Scale data\n",
    "scaler = StandardScaler()\n",
    "X_train_norm = scaler.fit_transform(X_train.drop(columns=['id', 'fitness_group']))\n",
    "X_test_norm = scaler.fit_transform(X_test.drop(columns=['id', 'fitness_group']))\n",
    "\n",
    "alphas = np.logspace(-6, 6, 13)  # Define a range of alphas for cross-validation\n",
    "ridge_cv = RidgeCV(alphas=alphas, store_cv_values=True)\n",
    "ridge_cv.fit(X_train_norm, y_train)\n",
    "\n",
    "# Predict the fitness for the test set\n",
    "y_pred_ridge = ridge_cv.predict(X_test_norm)\n",
    "\n",
    "# Print the best alpha and model performance\n",
    "print(f\"Best alpha: {ridge_cv.alpha_}\")\n",
    "print(f\"Training Score: {ridge_cv.score(X_train_norm, y_train)}\")\n",
    "print(f\"Test Score: {ridge_cv.score(X_test_norm, y_test)}\")\n",
    "\n",
    "# Predict and evaluate the model\n",
    "mse = mean_squared_error(y_test, y_pred_ridge)\n",
    "print(f\"Mean Squared Error: {mse}\")\n",
    "r2 = r2_score(y_test, y_pred_ridge)\n",
    "print(f\"R2 score: {r2}\")\n",
    "mae = mean_absolute_error(y_test, y_pred_ridge)\n",
    "print(f\"Mean Absolute Error: {mae}\")\n",
    "# Spearman's correl\n",
    "spearman_corr, spearman_p_value = spearmanr(y_test, y_pred_ridge)\n",
    "print(f\"Spearman's correlation: {spearman_corr}\")\n",
    "print(f\"Spearman's p-value: {spearman_p_value}\")\n",
    "\n",
    "# Plot actual fitness against predicted\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.scatter(y_test, y_pred_ridge, alpha=0.7)\n",
    "plt.xlim(-0.25, 4.25)\n",
    "plt.ylim(0, 2)\n",
    "plt.xlabel('Actual')\n",
    "plt.ylabel('Predicted')\n",
    "plt.title('Actual VS predicted fitness')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8c8faed8-3274-4be7-bf3c-ebf779dbbebf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>fitness</th>\n",
       "      <th>fitness_group</th>\n",
       "      <th>mean_pssm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>K30D:0.5080366134643555</td>\n",
       "      <td>0.508037</td>\n",
       "      <td>3</td>\n",
       "      <td>-2.092548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>D127S:0.619499683380127</td>\n",
       "      <td>0.619500</td>\n",
       "      <td>3</td>\n",
       "      <td>-2.090103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>L114Y:0.9718983173370361</td>\n",
       "      <td>0.971898</td>\n",
       "      <td>6</td>\n",
       "      <td>-2.095258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>H83F:0.5161952972412109</td>\n",
       "      <td>0.516195</td>\n",
       "      <td>3</td>\n",
       "      <td>-2.085923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R61G:0.7325457334518433</td>\n",
       "      <td>0.732546</td>\n",
       "      <td>4</td>\n",
       "      <td>-2.097639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2849</th>\n",
       "      <td>A15W:0.6964787244796753</td>\n",
       "      <td>0.696479</td>\n",
       "      <td>4</td>\n",
       "      <td>-2.096031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2850</th>\n",
       "      <td>P28F:0.5048689246177673</td>\n",
       "      <td>0.504869</td>\n",
       "      <td>3</td>\n",
       "      <td>-2.091682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2851</th>\n",
       "      <td>I112Y:0.28946661949157715</td>\n",
       "      <td>0.289467</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.086953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2852</th>\n",
       "      <td>P32W:0.9419150352478027</td>\n",
       "      <td>0.941915</td>\n",
       "      <td>6</td>\n",
       "      <td>-2.091496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2853</th>\n",
       "      <td>Q139W:0.7723677754402161</td>\n",
       "      <td>0.772368</td>\n",
       "      <td>4</td>\n",
       "      <td>-2.096764</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2854 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             id   fitness  fitness_group  mean_pssm\n",
       "0       K30D:0.5080366134643555  0.508037              3  -2.092548\n",
       "1       D127S:0.619499683380127  0.619500              3  -2.090103\n",
       "2      L114Y:0.9718983173370361  0.971898              6  -2.095258\n",
       "3       H83F:0.5161952972412109  0.516195              3  -2.085923\n",
       "4       R61G:0.7325457334518433  0.732546              4  -2.097639\n",
       "...                         ...       ...            ...        ...\n",
       "2849    A15W:0.6964787244796753  0.696479              4  -2.096031\n",
       "2850    P28F:0.5048689246177673  0.504869              3  -2.091682\n",
       "2851  I112Y:0.28946661949157715  0.289467              0  -2.086953\n",
       "2852    P32W:0.9419150352478027  0.941915              6  -2.091496\n",
       "2853   Q139W:0.7723677754402161  0.772368              4  -2.096764\n",
       "\n",
       "[2854 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Calculate the mean of the 3160 features for each sample and use that for model training###\n",
    "# Select features1-3160\n",
    "features_df = mutant_pssm_fitness.iloc[:, 3:] # the original MSA PSSM feature df with fitness scores and IDs\n",
    "\n",
    "# Calculate the mean across the selected columns for each row\n",
    "mean_pssm = features_df.mean(axis=1)\n",
    "\n",
    "# Add back id, fitness, fitness group\n",
    "mean_pssm_fitness = pd.DataFrame({\n",
    "    'id': mutant_pssm_fitness.iloc[:, 0],\n",
    "    'fitness': mutant_pssm_fitness.iloc[:, 1],\n",
    "    'fitness_group': mutant_pssm_fitness.iloc[:, 2],\n",
    "    'mean_pssm': mean_pssm\n",
    "})\n",
    "\n",
    "display(mean_pssm_fitness)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "11d05a64-ac97-408a-a576-8f497343f38d",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_pssm_fitness.to_csv('mean_pssm_fitness.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "553e80f2-848c-4dcf-977c-e89794855111",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best alpha: 10.0\n",
      "Training Score: 0.08021350581764519\n",
      "Test Score: 0.07476341145855736\n",
      "Mean Squared Error: 0.11665435475621476\n",
      "R2 score: 0.07476341145855736\n",
      "Mean Absolute Error: 0.26252787407027584\n",
      "Spearman's correlation: 0.33303079326303414\n",
      "Spearman's p-value: 2.020987099978103e-72\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ridge regr on a single mean of features\n",
    "\n",
    "# Use train_test_split with the stratify parameter\n",
    "X1 = mean_pssm_fitness.drop(columns=['fitness'])\n",
    "y1 = mean_pssm_fitness['fitness']\n",
    "\n",
    "X_train1, X_test1, y_train1, y_test1 = train_test_split(X1, y1, train_size=96, stratify=X1['fitness_group'], random_state=42)\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train1_norm = scaler.fit_transform(X_train1.drop(columns=['id', 'fitness_group']))\n",
    "X_test1_norm = scaler.fit_transform(X_test1.drop(columns=['id', 'fitness_group']))\n",
    "\n",
    "alphas = np.logspace(-6, 6, 13)  # Define a range of alphas for cross-validation\n",
    "ridge_cv = RidgeCV(alphas=alphas, store_cv_values=True)\n",
    "ridge_cv.fit(X_train1_norm, y_train1)\n",
    "\n",
    "# Predict the fitness for the test set\n",
    "y_pred_ridge1 = ridge_cv.predict(X_test1_norm)\n",
    "\n",
    "# Print the best alpha and model performance\n",
    "print(f\"Best alpha: {ridge_cv.alpha_}\")\n",
    "print(f\"Training Score: {ridge_cv.score(X_train1_norm, y_train1)}\")\n",
    "print(f\"Test Score: {ridge_cv.score(X_test1_norm, y_test1)}\")\n",
    "\n",
    "# Predict and evaluate the model\n",
    "mse = mean_squared_error(y_test1, y_pred_ridge1)\n",
    "print(f\"Mean Squared Error: {mse}\")\n",
    "r2 = r2_score(y_test1, y_pred_ridge1)\n",
    "print(f\"R2 score: {r2}\")\n",
    "mae = mean_absolute_error(y_test1, y_pred_ridge1)\n",
    "print(f\"Mean Absolute Error: {mae}\")\n",
    "# Spearman's correl\n",
    "spearman_corr, spearman_p_value = spearmanr(y_test1, y_pred_ridge1)\n",
    "print(f\"Spearman's correlation: {spearman_corr}\")\n",
    "print(f\"Spearman's p-value: {spearman_p_value}\")\n",
    "\n",
    "# Plot actual fitness against predicted\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.scatter(y_test1, y_pred_ridge1, alpha=0.7)\n",
    "plt.xlim(-0.25, 4.25)\n",
    "plt.ylim(0, 4.25)\n",
    "plt.xlabel('Actual')\n",
    "plt.ylabel('Predicted')\n",
    "plt.title('Actual VS predicted fitness')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9edf7d1c-58ab-48ae-a5e7-08ff10385fc3",
   "metadata": {},
   "source": [
    "### Considering the amount of features reduced, Spearman correlation barely dropped. Use only the mean of the PSSM matrix for further model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "44c0d0d3-0e67-4d97-9cb1-ad764dc9393f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>mut_id</th>\n",
       "      <th>fitness</th>\n",
       "      <th>Volume</th>\n",
       "      <th>MolWt</th>\n",
       "      <th>NumHeavyAtoms</th>\n",
       "      <th>Hydropathy</th>\n",
       "      <th>NumRotatableBonds</th>\n",
       "      <th>FractionCSP3</th>\n",
       "      <th>Acceptor</th>\n",
       "      <th>...</th>\n",
       "      <th>NumPolarRes</th>\n",
       "      <th>NumApolarRes</th>\n",
       "      <th>NumChargedRes</th>\n",
       "      <th>ProtRDF_all_2.0_3.0</th>\n",
       "      <th>ProtRDF_all_3.0_4.0</th>\n",
       "      <th>ProtRDF_all_4.0_5.0</th>\n",
       "      <th>RosettaEnergy</th>\n",
       "      <th>loss</th>\n",
       "      <th>fitness_group</th>\n",
       "      <th>mean_pssm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>K30D:0.5080366134643555</td>\n",
       "      <td>K30D</td>\n",
       "      <td>0.508037</td>\n",
       "      <td>-57.5</td>\n",
       "      <td>-13.087</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>-2</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>-11</td>\n",
       "      <td>-20</td>\n",
       "      <td>-11.954</td>\n",
       "      <td>0.181988</td>\n",
       "      <td>3</td>\n",
       "      <td>-2.092548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>D127S:0.619499683380127</td>\n",
       "      <td>D127S</td>\n",
       "      <td>0.619500</td>\n",
       "      <td>-22.1</td>\n",
       "      <td>-28.010</td>\n",
       "      <td>-2</td>\n",
       "      <td>2.7</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>-1</td>\n",
       "      <td>-12</td>\n",
       "      <td>8</td>\n",
       "      <td>-1.321</td>\n",
       "      <td>0.117051</td>\n",
       "      <td>3</td>\n",
       "      <td>-2.090103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>L114Y:0.9718983173370361</td>\n",
       "      <td>L114Y</td>\n",
       "      <td>0.971898</td>\n",
       "      <td>26.9</td>\n",
       "      <td>50.016</td>\n",
       "      <td>4</td>\n",
       "      <td>-5.1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.611111</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>29</td>\n",
       "      <td>7</td>\n",
       "      <td>-5</td>\n",
       "      <td>-16</td>\n",
       "      <td>-51</td>\n",
       "      <td>3.378</td>\n",
       "      <td>0.172539</td>\n",
       "      <td>6</td>\n",
       "      <td>-2.095258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>H83F:0.5161952972412109</td>\n",
       "      <td>H83F</td>\n",
       "      <td>0.516195</td>\n",
       "      <td>36.7</td>\n",
       "      <td>10.035</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.111111</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "      <td>48</td>\n",
       "      <td>33.264</td>\n",
       "      <td>0.211969</td>\n",
       "      <td>3</td>\n",
       "      <td>-2.085923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R61G:0.7325457334518433</td>\n",
       "      <td>R61G</td>\n",
       "      <td>0.732546</td>\n",
       "      <td>-113.3</td>\n",
       "      <td>-99.137</td>\n",
       "      <td>-7</td>\n",
       "      <td>4.1</td>\n",
       "      <td>-4</td>\n",
       "      <td>-0.166667</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>32</td>\n",
       "      <td>7</td>\n",
       "      <td>-21</td>\n",
       "      <td>-72</td>\n",
       "      <td>-171</td>\n",
       "      <td>-6.110</td>\n",
       "      <td>0.151326</td>\n",
       "      <td>4</td>\n",
       "      <td>-2.097639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2849</th>\n",
       "      <td>A15W:0.6964787244796753</td>\n",
       "      <td>A15W</td>\n",
       "      <td>0.696479</td>\n",
       "      <td>139.2</td>\n",
       "      <td>115.135</td>\n",
       "      <td>9</td>\n",
       "      <td>-2.7</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.484848</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>67</td>\n",
       "      <td>168</td>\n",
       "      <td>-13.282</td>\n",
       "      <td>0.207076</td>\n",
       "      <td>4</td>\n",
       "      <td>-2.096031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2850</th>\n",
       "      <td>P28F:0.5048689246177673</td>\n",
       "      <td>P28F</td>\n",
       "      <td>0.504869</td>\n",
       "      <td>77.2</td>\n",
       "      <td>50.060</td>\n",
       "      <td>4</td>\n",
       "      <td>4.4</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.577778</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>-1</td>\n",
       "      <td>13</td>\n",
       "      <td>55</td>\n",
       "      <td>-6.672</td>\n",
       "      <td>0.173218</td>\n",
       "      <td>3</td>\n",
       "      <td>-2.091682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2851</th>\n",
       "      <td>I112Y:0.28946661949157715</td>\n",
       "      <td>I112Y</td>\n",
       "      <td>0.289467</td>\n",
       "      <td>26.9</td>\n",
       "      <td>50.016</td>\n",
       "      <td>4</td>\n",
       "      <td>-5.8</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.611111</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>31</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>111.471</td>\n",
       "      <td>0.174368</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.086953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2852</th>\n",
       "      <td>P32W:0.9419150352478027</td>\n",
       "      <td>P32W</td>\n",
       "      <td>0.941915</td>\n",
       "      <td>115.1</td>\n",
       "      <td>89.097</td>\n",
       "      <td>7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.618182</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>-8</td>\n",
       "      <td>23</td>\n",
       "      <td>56</td>\n",
       "      <td>-13.450</td>\n",
       "      <td>0.201536</td>\n",
       "      <td>6</td>\n",
       "      <td>-2.091496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2853</th>\n",
       "      <td>Q139W:0.7723677754402161</td>\n",
       "      <td>Q139W</td>\n",
       "      <td>0.772368</td>\n",
       "      <td>84.0</td>\n",
       "      <td>58.083</td>\n",
       "      <td>5</td>\n",
       "      <td>2.6</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.418182</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>-10.997</td>\n",
       "      <td>0.217409</td>\n",
       "      <td>4</td>\n",
       "      <td>-2.096764</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2854 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             id mut_id   fitness  Volume    MolWt  \\\n",
       "0       K30D:0.5080366134643555   K30D  0.508037   -57.5  -13.087   \n",
       "1       D127S:0.619499683380127  D127S  0.619500   -22.1  -28.010   \n",
       "2      L114Y:0.9718983173370361  L114Y  0.971898    26.9   50.016   \n",
       "3       H83F:0.5161952972412109   H83F  0.516195    36.7   10.035   \n",
       "4       R61G:0.7325457334518433   R61G  0.732546  -113.3  -99.137   \n",
       "...                         ...    ...       ...     ...      ...   \n",
       "2849    A15W:0.6964787244796753   A15W  0.696479   139.2  115.135   \n",
       "2850    P28F:0.5048689246177673   P28F  0.504869    77.2   50.060   \n",
       "2851  I112Y:0.28946661949157715  I112Y  0.289467    26.9   50.016   \n",
       "2852    P32W:0.9419150352478027   P32W  0.941915   115.1   89.097   \n",
       "2853   Q139W:0.7723677754402161  Q139W  0.772368    84.0   58.083   \n",
       "\n",
       "      NumHeavyAtoms  Hydropathy  NumRotatableBonds  FractionCSP3  Acceptor  \\\n",
       "0                -1         0.4                 -2     -0.333333         2   \n",
       "1                -2         2.7                 -1      0.166667        -1   \n",
       "2                 4        -5.1                  0     -0.611111         1   \n",
       "3                 1         6.0                  0     -0.111111        -1   \n",
       "4                -7         4.1                 -4     -0.166667        -1   \n",
       "...             ...         ...                ...           ...       ...   \n",
       "2849              9        -2.7                  2     -0.484848         0   \n",
       "2850              4         4.4                  2     -0.577778         0   \n",
       "2851              4        -5.8                  0     -0.611111         1   \n",
       "2852              7         0.7                  2     -0.618182         0   \n",
       "2853              5         2.6                 -1     -0.418182        -1   \n",
       "\n",
       "      ...  NumPolarRes  NumApolarRes  NumChargedRes  ProtRDF_all_2.0_3.0  \\\n",
       "0     ...            6             9              3                    1   \n",
       "1     ...           13            14              5                   -1   \n",
       "2     ...           10            29              7                   -5   \n",
       "3     ...           19            31             12                    8   \n",
       "4     ...            9            32              7                  -21   \n",
       "...   ...          ...           ...            ...                  ...   \n",
       "2849  ...            8            13              8                    9   \n",
       "2850  ...           11            20              8                   -1   \n",
       "2851  ...           14            31             11                   17   \n",
       "2852  ...            5             4              3                   -8   \n",
       "2853  ...            7             7              5                    1   \n",
       "\n",
       "      ProtRDF_all_3.0_4.0  ProtRDF_all_4.0_5.0  RosettaEnergy      loss  \\\n",
       "0                     -11                  -20        -11.954  0.181988   \n",
       "1                     -12                    8         -1.321  0.117051   \n",
       "2                     -16                  -51          3.378  0.172539   \n",
       "3                      24                   48         33.264  0.211969   \n",
       "4                     -72                 -171         -6.110  0.151326   \n",
       "...                   ...                  ...            ...       ...   \n",
       "2849                   67                  168        -13.282  0.207076   \n",
       "2850                   13                   55         -6.672  0.173218   \n",
       "2851                   21                    6        111.471  0.174368   \n",
       "2852                   23                   56        -13.450  0.201536   \n",
       "2853                   12                   13        -10.997  0.217409   \n",
       "\n",
       "      fitness_group  mean_pssm  \n",
       "0                 3  -2.092548  \n",
       "1                 3  -2.090103  \n",
       "2                 6  -2.095258  \n",
       "3                 3  -2.085923  \n",
       "4                 4  -2.097639  \n",
       "...             ...        ...  \n",
       "2849              4  -2.096031  \n",
       "2850              3  -2.091682  \n",
       "2851              0  -2.086953  \n",
       "2852              6  -2.091496  \n",
       "2853              4  -2.096764  \n",
       "\n",
       "[2854 rows x 26 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Merge with biophysical features and using mean_pssm and top 11 biophysical features selected previously, run RFR and RR\n",
    "features = pd.read_csv(\"features.csv\")\n",
    "features[['mut_id', 'fitness']] = features['id'].str.split(':', expand=True)\n",
    "features = features[['id', 'mut_id', 'fitness'] + [col for col in features.columns if col not in ['id', 'mut_id', 'fitness']]]\n",
    "features['fitness'] = pd.to_numeric(features['fitness'])\n",
    "\n",
    "# Merge with features_top on 'fitness'\n",
    "features_pssm = pd.merge(features, mean_pssm_fitness, on='id', how='inner')\n",
    "# Drop duplicates (checked to make sure it's the same) and rename _x to original\n",
    "features_pssm = features_pssm.drop(columns=['fitness_y'])\n",
    "features_pssm = features_pssm.rename(columns={'fitness_x': 'fitness'})\n",
    "display(features_pssm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f4278714-e670-4a3f-9b90-a7b3d5523827",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best alpha: 100.0\n",
      "Training Score: 0.3461378843468901\n",
      "Test Score: 0.125008847315787\n",
      "Mean Squared Error: 0.11031938165640487\n",
      "R2 score: 0.125008847315787\n",
      "Mean Absolute Error: 0.24858404509240656\n",
      "Spearman's correlation: 0.4247192174234317\n",
      "Spearman's p-value: 3.20888206257661e-121\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Drop bottom features as determined by RFR\n",
    "X = features_pssm.drop(columns=['mut_id', 'fitness', 'loss', 'Aromatic', 'NetCharge', 'LumpedHydrophobe', 'Donor', 'NumHeavyAtoms', 'Acceptor', 'NumChargedRes', 'Hydrophobe', 'ProtRDF_all_4.0_5.0']) #Still contains 'id' and 'fitness_group'columns\n",
    "y = features_pssm['fitness']\n",
    "\n",
    "poly = PolynomialFeatures(degree=2, include_bias=False)\n",
    "X_poly = poly.fit_transform(X.drop(columns=['id', 'fitness_group']))\n",
    "\n",
    "# Scale\n",
    "minmaxscaler = MinMaxScaler()\n",
    "scaler = StandardScaler()\n",
    "X_poly_norm = scaler.fit_transform(X_poly)\n",
    "X_poly_minmax = minmaxscaler.fit_transform(X_poly)\n",
    "\n",
    "# Sample 96 samples, in a stratified manner, based on fitness_group\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_poly_norm, y, train_size=96, stratify=X['fitness_group'], random_state=42)\n",
    "\n",
    "# Define a range of alphas for cross-validation\n",
    "alphas = np.logspace(-6, 6, 13)  \n",
    "ridge_cv_poly = RidgeCV(alphas=alphas, store_cv_values=True)\n",
    "ridge_cv_poly.fit(X_train, y_train)\n",
    "\n",
    "# Predict the fitness for the test set\n",
    "y_pred_ridge_poly = ridge_cv_poly.predict(X_test)\n",
    "\n",
    "# Print the best alpha and model performance\n",
    "print(f\"Best alpha: {ridge_cv_poly.alpha_}\")\n",
    "print(f\"Training Score: {ridge_cv_poly.score(X_train, y_train)}\")\n",
    "print(f\"Test Score: {ridge_cv_poly.score(X_test, y_test)}\")\n",
    "\n",
    "# Predict and evaluate the model\n",
    "mse = mean_squared_error(y_test, y_pred_ridge_poly)\n",
    "print(f\"Mean Squared Error: {mse}\")\n",
    "r2 = r2_score(y_test, y_pred_ridge_poly)\n",
    "print(f\"R2 score: {r2}\")\n",
    "mae = mean_absolute_error(y_test, y_pred_ridge_poly)\n",
    "print(f\"Mean Absolute Error: {mae}\")\n",
    "# Spearman's correl\n",
    "spearman_corr, spearman_p_value = spearmanr(y_test, y_pred_ridge_poly)\n",
    "print(f\"Spearman's correlation: {spearman_corr}\")\n",
    "print(f\"Spearman's p-value: {spearman_p_value}\")\n",
    "\n",
    "# Plot predicted VS actal fitness\n",
    "plt.scatter(y_test, y_pred_ridge_poly, alpha=0.5)\n",
    "plt.xlabel('Predicted Values')\n",
    "plt.ylim(-0.5, 4.25)\n",
    "plt.ylabel('Actual Values')\n",
    "plt.title('Actual VS Predicted fitness')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "25f0e866-fdd2-467c-9cb5-9f2f317e87a7",
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Volume</th>\n",
       "      <th>MolWt</th>\n",
       "      <th>Hydropathy</th>\n",
       "      <th>NumRotatableBonds</th>\n",
       "      <th>FractionCSP3</th>\n",
       "      <th>Charge</th>\n",
       "      <th>NumPolarRes</th>\n",
       "      <th>NumApolarRes</th>\n",
       "      <th>ProtRDF_all_2.0_3.0</th>\n",
       "      <th>ProtRDF_all_3.0_4.0</th>\n",
       "      <th>RosettaEnergy</th>\n",
       "      <th>fitness_group</th>\n",
       "      <th>mean_pssm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>743</th>\n",
       "      <td>F77S:0.34993505477905273</td>\n",
       "      <td>-100.9</td>\n",
       "      <td>-60.099</td>\n",
       "      <td>-3.6</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>37</td>\n",
       "      <td>-33</td>\n",
       "      <td>-110</td>\n",
       "      <td>-2.958</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.086381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1451</th>\n",
       "      <td>P73N:0.37244078516960144</td>\n",
       "      <td>1.4</td>\n",
       "      <td>16.987</td>\n",
       "      <td>-1.9</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.300000</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>-12</td>\n",
       "      <td>1.595</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.079922</td>\n",
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       "    <tr>\n",
       "      <th>2247</th>\n",
       "      <td>L57Q:0.9007079005241394</td>\n",
       "      <td>-22.9</td>\n",
       "      <td>14.971</td>\n",
       "      <td>-7.3</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.233333</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>29</td>\n",
       "      <td>-11</td>\n",
       "      <td>-32</td>\n",
       "      <td>-11.100</td>\n",
       "      <td>5</td>\n",
       "      <td>-2.098448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2281</th>\n",
       "      <td>D102W:0.8114498257637024</td>\n",
       "      <td>116.7</td>\n",
       "      <td>71.126</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.318182</td>\n",
       "      <td>-2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>-7.367</td>\n",
       "      <td>5</td>\n",
       "      <td>-2.096282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>T29G:0.9752710461616516</td>\n",
       "      <td>-56.0</td>\n",
       "      <td>-44.053</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.250000</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>-13</td>\n",
       "      <td>-62</td>\n",
       "      <td>-11.643</td>\n",
       "      <td>6</td>\n",
       "      <td>-2.097281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2025</th>\n",
       "      <td>F77H:0.3387545049190521</td>\n",
       "      <td>-36.7</td>\n",
       "      <td>-10.035</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>37</td>\n",
       "      <td>-17</td>\n",
       "      <td>-35</td>\n",
       "      <td>-8.135</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.086934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2500</th>\n",
       "      <td>L94N:0.44525304436683655</td>\n",
       "      <td>-52.6</td>\n",
       "      <td>0.944</td>\n",
       "      <td>-7.3</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>-2</td>\n",
       "      <td>14</td>\n",
       "      <td>27</td>\n",
       "      <td>-5</td>\n",
       "      <td>-56</td>\n",
       "      <td>-12.894</td>\n",
       "      <td>2</td>\n",
       "      <td>-2.088909</td>\n",
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       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>L38F:0.8292379379272461</td>\n",
       "      <td>23.2</td>\n",
       "      <td>34.017</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.611111</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>-7</td>\n",
       "      <td>-21</td>\n",
       "      <td>-11.468</td>\n",
       "      <td>5</td>\n",
       "      <td>-2.091405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1174</th>\n",
       "      <td>A156F:0.8652383685112</td>\n",
       "      <td>101.3</td>\n",
       "      <td>76.098</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.444444</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>-9.789</td>\n",
       "      <td>5</td>\n",
       "      <td>-2.085013</td>\n",
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       "    <tr>\n",
       "      <th>2362</th>\n",
       "      <td>W53D:0.4444430470466614</td>\n",
       "      <td>-116.7</td>\n",
       "      <td>-71.126</td>\n",
       "      <td>-2.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.318182</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>32</td>\n",
       "      <td>-32</td>\n",
       "      <td>-128</td>\n",
       "      <td>-17.581</td>\n",
       "      <td>2</td>\n",
       "      <td>-2.084987</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            id  Volume   MolWt  Hydropathy  NumRotatableBonds  \\\n",
       "743   F77S:0.34993505477905273  -100.9 -60.099        -3.6                 -1   \n",
       "1451  P73N:0.37244078516960144     1.4  16.987        -1.9                  2   \n",
       "2247   L57Q:0.9007079005241394   -22.9  14.971        -7.3                  1   \n",
       "2281  D102W:0.8114498257637024   116.7  71.126         2.6                  0   \n",
       "544    T29G:0.9752710461616516   -56.0 -44.053         0.3                 -1   \n",
       "...                        ...     ...     ...         ...                ...   \n",
       "2025   F77H:0.3387545049190521   -36.7 -10.035        -6.0                  0   \n",
       "2500  L94N:0.44525304436683655   -52.6   0.944        -7.3                  0   \n",
       "480    L38F:0.8292379379272461    23.2  34.017        -1.0                  0   \n",
       "1174     A156F:0.8652383685112   101.3  76.098         1.0                  2   \n",
       "2362   W53D:0.4444430470466614  -116.7 -71.126        -2.6                  0   \n",
       "\n",
       "      FractionCSP3  Charge  NumPolarRes  NumApolarRes  ProtRDF_all_2.0_3.0  \\\n",
       "743       0.444444       1           16            37                  -33   \n",
       "1451     -0.300000       1           13            26                    5   \n",
       "2247     -0.233333       4            7            29                  -11   \n",
       "2281     -0.318182      -2            9            12                    3   \n",
       "544      -0.250000       1            6            16                  -13   \n",
       "...            ...     ...          ...           ...                  ...   \n",
       "2025      0.111111       1           16            37                  -17   \n",
       "2500     -0.333333      -2           14            27                   -5   \n",
       "480      -0.611111       4           12            20                   -7   \n",
       "1174     -0.444444       3            2            17                   12   \n",
       "2362      0.318182       4           12            32                  -32   \n",
       "\n",
       "      ProtRDF_all_3.0_4.0  RosettaEnergy  fitness_group  mean_pssm  \n",
       "743                  -110         -2.958              1  -2.086381  \n",
       "1451                  -12          1.595              1  -2.079922  \n",
       "2247                  -32        -11.100              5  -2.098448  \n",
       "2281                   19         -7.367              5  -2.096282  \n",
       "544                   -62        -11.643              6  -2.097281  \n",
       "...                   ...            ...            ...        ...  \n",
       "2025                  -35         -8.135              0  -2.086934  \n",
       "2500                  -56        -12.894              2  -2.088909  \n",
       "480                   -21        -11.468              5  -2.091405  \n",
       "1174                   31         -9.789              5  -2.085013  \n",
       "2362                 -128        -17.581              2  -2.084987  \n",
       "\n",
       "[96 rows x 14 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 0.10846081520528092\n",
      "R2 score: 0.1397499488066758\n",
      "Mean Absolute Error: 0.24571161511364958\n",
      "Spearman's correlation: 0.4203249000100723\n",
      "Spearman's p-value: 1.6448964363241758e-118\n"
     ]
    }
   ],
   "source": [
    "# RFR\n",
    "# Drop bottom features as determined by RFR\n",
    "X = features_pssm.drop(columns=['mut_id', 'fitness', 'loss', 'Aromatic', 'NetCharge', 'LumpedHydrophobe', 'Donor', 'NumHeavyAtoms', 'Acceptor', 'NumChargedRes', 'Hydrophobe', 'ProtRDF_all_4.0_5.0']) #Still contains 'id' and 'fitness_group'columns\n",
    "y = features_pssm['fitness']\n",
    "\n",
    "# Use train_test_split with the stratify parameter\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=96, stratify=X['fitness_group'], random_state=42)\n",
    "\n",
    "display(X_train)\n",
    "\n",
    "# Train the rfr\n",
    "rfr = RandomForestRegressor(random_state=42)\n",
    "rfr.fit(X_train.drop(columns=['id', 'fitness_group']), y_train)\n",
    "\n",
    "# Predict the fitness scores for the test set\n",
    "y_pred = rfr.predict(X_test.drop(columns=['id', 'fitness_group']))\n",
    "\n",
    "# Evaluate the model\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(f\"Mean Squared Error: {mse}\")\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "print(f\"R2 score: {r2}\")\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "print(f\"Mean Absolute Error: {mae}\")\n",
    "\n",
    "# Spearman's correl\n",
    "spearman_corr, spearman_p_value = spearmanr(y_test, y_pred)\n",
    "print(f\"Spearman's correlation: {spearman_corr}\")\n",
    "print(f\"Spearman's p-value: {spearman_p_value}\")"
   ]
  }
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