Skip to content

Instantly share code, notes, and snippets.

@lewisacidic

lewisacidic/RDkit_Introduction

Created March 6, 2014 17:21
Show Gist options
  • Save lewisacidic/9394753 to your computer and use it in GitHub Desktop.
Save lewisacidic/9394753 to your computer and use it in GitHub Desktop.
Download ZIP
Raw
RDkit_Introduction
{
"worksheets": [
{
"cells": [
{
"metadata": {},
"cell_type": "markdown",
"source": "### Software Workshop Cambridge Nov. 2013\n# Introduction to RDkit, Scikit and pandas\n\nAuthor: **Isidro Cortés Ciriano** (isidrolauscher@gmail.com) \n\nReformatted by: **Rich Lewis** (rl403@cam.ac.uk)\n\nAcknowledgements: Greg Landrum and the RDkit mailing list\n\nSee also rdkit.org and http://www.rdkit.org/docs/GettingStartedInPython.html for further information"
},
{
"metadata": {},
"input": "import numpy as np\nimport os,sys\nimport gzip\nimport rdkit\nfrom rdkit import Chem\nfrom rdkit.Chem import AllChem\nimport rdkit.rdBase\nfrom rdkit.Chem.MACCSkeys import GenMACCSKeys\nfrom rdkit import DataStructs\nfrom rdkit.DataStructs import BitVectToText\nfrom rdkit.Chem.Draw import IPythonConsole\nfrom rdkit.Chem import Draw\nfrom rdkit import DataStructs\nfrom rdkit.Chem import MCS as MCS\nfrom rdkit.Chem import Descriptors as Descriptors\nfrom rdkit.ML.Descriptors import MoleculeDescriptors\nimport pandas as pd \nfrom rdkit.Chem import PandasTools as PandasTools\nfrom rdkit.Chem import Descriptors as Descriptors\nimport pylab as plt\n\n# Default pylab parameters\nplt.rcParams['figure.figsize'] = 8,6\n# figure resolution when saving to file\n#rcParams['savefig.dpi'] = 120",
"cell_type": "code",
"prompt_number": 2,
"outputs": [],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": " # Section 1. RDkit Overview"
},
{
"metadata": {},
"cell_type": "markdown",
"source": " Molecules are saved in mol objects, be them read individually or in groups:\n\n Individually:"
},
{
"metadata": {},
"input": "m = Chem.MolFromSmiles('Cc1ccccc1')\n# To check that it was loaded properly:\nm is None\n# True if there was a problem",
"cell_type": "code",
"prompt_number": 3,
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": "False"
}
],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Reading files with a set of molecules:\n"
},
{
"metadata": {},
"input": "# A) SDF file\nmols = Chem.SDMolSupplier('/Users/RichLewis/Dropbox/PhD/Data/Molecules/data_set.sdf')\n# or\n# B) Smiles file\nmols_smiles = Chem.SmilesMolSupplier('/Users/RichLewis/Dropbox/PhD/Data/Molecules/smiles_example.smi')\n\nmol_example = mols_smiles[1]\n\nprint mol_example",
"cell_type": "code",
"prompt_number": 11,
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "None\n"
}
],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": " We can iterate over the molecules and get their properties\n ```e.g.\n for mol in mols:\n print mol.GetNumAtoms()```\n\n Or convert them into a list of mol objects"
},
{
"metadata": {},
"input": "mols = [x for x in mols]\n\n# GOOD PRACTICE:\n# Check if the molecules were read properly:\n# A. when creating the list:\nmols = [x for x in mols if x is not None]\n\n# B. when iterating, so we can identify erroneous molecules\nfor i,mol in enumerate(mols):\n if mol is None: \n print \"Molecule \",i,\"is erroneous\" \n continue # When it finds an incorrect molecule, it continues to the next element of mols\n #print mol.GetNumAtoms() # we do sthg on each molecule. e.g. print a the number of atoms",
"cell_type": "code",
"prompt_number": 6,
"outputs": [],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "We can also read molecules from a compressed file:\n\n```import gzip\ncompressed_mols = gzip.open('')\nmols_gz = Chem.ForwardSDMolSupplier(compressed_mols)\nmols = [x for x in mols_gz if x is not None] ```"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Writing Molecules:\n"
},
{
"metadata": {},
"input": "Chem.MolToSmiles(mol_example)\n\n# A molecule can be kekulized and then, the kekulized smiles written\nChem.Kekulize(mol_example)\nChem.MolToSmiles(mol_example,kekuleSmiles=True)",
"cell_type": "code",
"prompt_number": 8,
"outputs": [
{
"ename": "ArgumentError",
"evalue": "Python argument types in\n rdkit.Chem.rdmolfiles.MolToSmiles(NoneType)\ndid not match C++ signature:\n MolToSmiles(RDKit::ROMol mol, bool isomericSmiles=False, bool kekuleSmiles=False, int rootedAtAtom=-1, bool canonical=True, bool allBondsExplicit=False)",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mArgumentError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-8-7582f4f603f4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mChem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMolToSmiles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmol_example\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m# A molecule can be kekulized and then, the kekulized smiles written\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mChem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mKekulize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmol_example\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mChem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMolToSmiles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmol_example\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mkekuleSmiles\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mArgumentError\u001b[0m: Python argument types in\n rdkit.Chem.rdmolfiles.MolToSmiles(NoneType)\ndid not match C++ signature:\n MolToSmiles(RDKit::ROMol mol, bool isomericSmiles=False, bool kekuleSmiles=False, int rootedAtAtom=-1, bool canonical=True, bool allBondsExplicit=False)"
]
}
],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Adding and removing H:"
},
{
"metadata": {},
"input": "mol_exampleH = Chem.AddHs(mol_example)\nmol_examplenoH = Chem.RemoveHs(mol_exampleH)\n\nprint mol_example.GetNumAtoms(), mol_exampleH.GetNumAtoms(), mol_examplenoH.GetNumAtoms()",
"cell_type": "code",
"prompt_number": 217,
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "4 8 4\n"
}
],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "A molecule can also be depicted and its properties changed\n"
},
{
"metadata": {},
"input": "print Chem.MolToMolBlock(mol_example) ",
"cell_type": "code",
"prompt_number": 218,
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "\n RDKit \n\n 4 3 0 0 0 0 0 0 0 0999 V2000\n 0.0000 0.0000 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0\n 0.0000 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\n 0.0000 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0\n 0.0000 0.0000 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0\n 1 2 2 0\n 2 3 1 0\n 2 4 1 0\nM END\n\n"
}
],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Writing Molecules to a file\n\n"
},
{
"metadata": {},
"input": "print mol_example.GetNumAtoms()\nprint >>file('test_write.block','w+'),Chem.MolToMolBlock(mol_example)\n\n# AGAIN: ALWAYS CHECK THAT THE MOLECULES ARE CORRECT\nmols = Chem.SDMolSupplier('data_set.sdf')\nmols = [x for x in mols]\nmols2 = [x for x in mols if x is not None]\n\nprint len(mols), len(mols2)\n\n# writing in .sdf format:\nw = Chem.SDWriter('test_write_set.sdf')\nfor m in mols2: w.write(m)\n\n# Writing incorrect set of molecules (an error arises)\n#w = Chem.SDWriter('test_write_set_INCORRECT.sdf')\n#for m in mols: w.write(m)\n\n# Writing in .smi (.smiles) format:\nw = Chem.SmilesWriter('test_write_set.smi')\nfor m in mols2: w.write(m)",
"cell_type": "code",
"prompt_number": 219,
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "4\n1290"
},
{
"output_type": "stream",
"stream": "stdout",
"text": " 1289\n"
}
],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "We can get properties for each molecule when we have read a .sdf file\n"
},
{
"metadata": {},
"input": "logS_correct = [float(mols2[x].GetProp('logS')) for x in range(0,len(mols2))]\n\nfor i,m in enumerate(mols2):\n m.SetProp(\"Source\",\"Workshop\")\n m.SetProp(\"Name\",\"Example Molecule No %d\" %(i))\n m.SetProp(\"LogS_Workshop\", \"%f\" % (logS_correct[i]))\n \n# To know which properties we have:\nprint list(mols2[0].GetPropNames())\n\n# Writing molecules with new propeties\nw = Chem.SDWriter('mols_OK_with_properties.sdf')\nfor m in mols2: w.write(m)",
"cell_type": "code",
"prompt_number": 269,
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "['CAS_Number', 'LogS_Workshop', 'Name', 'Source', 'logS']\n"
}
],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "##Depicting Molecules\n\nWe compute the 2D coordinates"
},
{
"metadata": {},
"input": "mols = [x for x in Chem.SmilesMolSupplier('example_set.smi') if x is not None]\nfor m in mols: AllChem.Compute2DCoords(m)\nfor i,m in enumerate(mols):\n m.SetProp(\"Name\",\"Example Molecules Set No %d\" %(i + 1))\n\np = Draw.MolsToGridImage( [mols[x] for x in range(0,3)] , legends=[x.GetProp(\"Name\") for x in mols] )\np",
"cell_type": "code",
"prompt_number": 4,
"outputs": [
{
"ename": "NameError",
"evalue": "name 'Chem' is not defined",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-4-09de845dbd44>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mChem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSmilesMolSupplier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'example_set.smi'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mm\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmols\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAllChem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCompute2DCoords\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mm\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmols\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSetProp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Name\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"Example Molecules Set No %d\"\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'Chem' is not defined"
]
}
],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"input": "## Similarity Calculations\n###\n\n# We calculate Morgan fingerprints (more on that later)\nmols_fps=[AllChem.GetMorganFingerprintAsBitVect(x,2) for x in mols]\nprint DataStructs.TanimotoSimilarity(mols_fps[0],mols_fps[0])\nprint DataStructs.TanimotoSimilarity(mols_fps[0],mols_fps[1])\n\n# Calculate similarities to reference molecule\nsim_ref = DataStructs.BulkTanimotoSimilarity(mols_fps[0],mols_fps)\n\n\n# Calculate pairwise similarities\ndef pairwise_sim(mols):\n pairwise=[]\n for mol in mols:\n sim = DataStructs.BulkTanimotoSimilarity(mol,mols)\n pairwise.append(sim)\n return pairwise\n\nPW = pairwise_sim(mols_fps)\nfigure,(plt1,plt2) = pylab.subplots(1,2)\nfigure.set_size_inches(15,5)\nplt1.hist(sim_ref)\nplt1.set_title('Similarity to reference molecule')\nplt2.hist(PW)\nplt2.set_title('Pairwise similarity all molecules')",
"cell_type": "code",
"prompt_number": 222,
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "1.0\n0.541666666667\n"
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 222,
"text": "<matplotlib.text.Text at 0x10f898b90>"
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAA2MAAAFCCAYAAAB8XgWzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
"text": "<matplotlib.figure.Figure at 0x10f7adc10>"
}
],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"input": "# sorting the molecule as a function of their similarity to a query\nnbrs = sorted(zip(sim_ref,mols),reverse=True)\n\n# We depict the molecules in decreasing order of similarity\nDraw.MolsToGridImage( [nbrs[x][1] for x in range(0,len(nbrs))] , legends=[nbrs[x][1].GetProp(\"Name\") for x in range(0,len(nbrs))])",
"cell_type": "code",
"prompt_number": 223,
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"png": "iVBORw0KGgoAAAANSUhEUgAAAlgAAASwCAIAAACow6NeAABW2klEQVR4nO392dHrypUG2oIVcuKU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\nrkJggg==\n",
"prompt_number": 223,
"text": "<Image.Image image mode=RGB size=600x1200 at 0x10DB33AB8>"
}
],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Finding the Maximum Common Sumstructure (MCS)"
},
{
"metadata": {},
"input": "mcs=MCS.FindMCS(mols)\n# mcs is a smarts pattern\nprint mcs\nChem.MolFromSmarts(mcs.smarts)",
"cell_type": "code",
"prompt_number": 224,
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "MCS '[#6]-[#6](-[#7]=[#6](-[#7])-[#7]=[#6](-[#7]-[#6]:1:[#6]:[#6]:[#6]:[#6]:[#6]:1)-[#7])-[#6]' has 16 atoms and 16 bonds\n"
},
{
"metadata": {},
"output_type": "pyout",
"png": "iVBORw0KGgoAAAANSUhEUgAAAcIAAACWCAIAAADCEh9HAAAHnUlEQVR4nO3d0ZaiOBQFUJg1///L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\nAAAASUVORK5CYII=\n",
"prompt_number": 224,
"text": "<rdkit.Chem.rdchem.Mol at 0x10f7aefa0>"
}
],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "We can also find the MCS without considering the atom type:\n"
},
{
"metadata": {},
"input": "mcs=MCS.FindMCS(mols,atomCompare=\"any\")\nChem.MolFromSmarts(mcs.smarts)",
"cell_type": "code",
"prompt_number": 225,
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"png": "iVBORw0KGgoAAAANSUhEUgAAAcIAAACWCAIAAADCEh9HAAAG0UlEQVR4nO3d3ZLiNhAGUDaV94Y3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\nBQgRowAhYhQgRIwChPwD7RDwFKhXGT0AAAAASUVORK5CYII=\n",
"prompt_number": 225,
"text": "<rdkit.Chem.rdchem.Mol at 0x10f7aef30>"
}
],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Section 2. Descriptor calculations\nReading the data as before:\n"
},
{
"metadata": {},
"input": "mols = Chem.SDMolSupplier('mols_OK_with_properties.sdf')\nmols = [x for x in mols if mol is not None]\nlogS_correct = [ np.float(mols[x].GetProp('logS')) for x in range(0,1289) ]\nprint len(logS_correct)\nprint list(mols[0].GetPropNames())",
"cell_type": "code",
"prompt_number": 282,
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "1289\n['CAS_Number', 'LogS_Workshop', 'Name', 'Source', 'logS']\n"
}
],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Calculate physicochemical descriptors for the molecules"
},
{
"metadata": {},
"input": "Desc_values = []\ndescriptors = []\n\n# iterate over all available descriptots\nfor D in Descriptors._descList:\n descriptors.append(D[0]) \ncalculator = MoleculeDescriptors.MolecularDescriptorCalculator(descriptors)\n\nfor sample in mols:\n use = True\n try:\n pattern = calculator.CalcDescriptors(sample)\n \n except:\n use = False\n print 'Error computing descriptor %s for %s'%(D[0],sample.GetProp('Name'))\n \n if use:\n Desc_values.append(pattern)",
"cell_type": "code",
"prompt_number": 1,
"outputs": [
{
"output_type": "pyerr",
"ename": "NameError",
"evalue": "name 'Descriptors' is not defined",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-63718adf4d6a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# iterate over all available descriptots\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mD\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mDescriptors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_descList\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mdescriptors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mD\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mcalculator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMoleculeDescriptors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMolecularDescriptorCalculator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdescriptors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'Descriptors' is not defined"
]
}
],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Calculate Morgan FPs:\n"
},
{
"metadata": {},
"input": "Morgan_fps = []\nfor sample in mols:\n use = True\n try:\n fp = AllChem.GetMorganFingerprintAsBitVect(sample,2,nBits=512)\n\n \n except:\n use = False\n print 'Error computing descriptor %s for %s'%(D[0],sample.GetProp('Name'))\n \n if use:\n Morgan_fps.append(fp)",
"cell_type": "code",
"prompt_number": 251,
"outputs": [],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Conversion of python list to np array:\n"
},
{
"metadata": {},
"input": "print np.shape(Desc_values)\nprint type(Desc_values)\nDesc_values = np.array(Desc_values)\nprint type(Desc_values)\nprint Desc_values.shape\nMorgan_fps = np.array(Morgan_fps)",
"cell_type": "code",
"prompt_number": 252,
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "(1289, 188)\n<type 'numpy.ndarray'>\n<type 'numpy.ndarray'>\n(1289, 188)\n"
}
],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Calculating unhashed circular Morgan FPs:\n"
},
{
"metadata": {},
"input": "nbBits=512\nfp_rad=2\nmols=mols\nnbMols = len(mols)\n\n# To search within sublists:\ndef insublist(item, list):\n for l in list:\n if np.array_equal(item,l):\n return True\n return False\n\n#declare the vector of zeros to know which positions have appeared\nposition_track=[0]*nbBits\n\n# Define variable to keep the smiles and bit numbers\narr = [[],[[]]]\nfor i in range(nbBits):\n arr[0].append(i)\n\nfor i in range(nbBits):\n arr[1].append([''])\n\n# Define variable to keep the smiles and bit numbers\narr2 = [[],[[]]]\nfor i in range(nbBits):\n arr2[0].append(i)\n\nfor i in range(nbBits):\n arr2[1].append([''])\n\n\n# Define the list of lists containing for each compound, the features that it contains.\nfps_by_comp=[[]]\nfor i in range(nbMols):\n fps_by_comp[0].append([''])\n\n\n# Define the list that will contain all the submolecules\nsubm_all =[]\n\nnbFeatTot=0\n#Loop over the molecules\nfor molecule_nb,m in enumerate(mols):\n info={}; info2={}\n if m is None:\t\n print \"Erroneous input at molecule: %d\" %(molecule_nb)\n continue\n\n fp = AllChem.GetMorganFingerprintAsBitVect(m,fp_rad,nbBits,bitInfo=info) \n AllBits=np.asarray([info.items()[i][0] for i in range(0,len(info.items()))])\n\n fp_bits=BitVectToText(fp)\n fp_counts=list(fp_bits)\n AtomRadBits=np.asarray([info.items()[p][1][:] for p in range(0,len(info.items()))])\n\n fp2 = AllChem.GetMorganFingerprint(m,fp_rad,bitInfo=info2)\n ids_now=np.asarray([info2.items()[i][0] for i in range(0,len(info2.items()))])\n ids_nowMOD=ids_now%nbBits\n AtomRadNow=np.asarray([info2.items()[p][1][:] for p in range(0,len(info2.items()))])\n\n for i in range(0,len(info2.items())):\n radius=info2.items()[i][1][0][1]\n atom=info2.items()[i][1][0][0]\n\n for k in range(0,len(AllBits)):\n if insublist(AtomRadNow[i][0],AtomRadBits[k]): \n bit = AllBits[k]\n break\n\n counts=len(info2.items()[i][1])\n if counts > 1: \n fp_counts[bit]=str(counts)\n\n if position_track[bit] == 0: \n position_track[bit]=1\n env=Chem.FindAtomEnvironmentOfRadiusN(m,radius,atom)\n amap={}\n submol=Chem.PathToSubmol(m,env,atomMap=amap)\n if len(amap)==0: # This means that the radius is zero, so the feature is a single atom\n arr[1][bit].append(ids_now[i]) #submol\n\n if ids_now[i] not in subm_all:\n nbFeatTot+=1 \n subm_all.append(ids_now[i] )\n # For each molecule keep the substructures\n fps_by_comp[0][molecule_nb].append(ids_now[i] )\n arr2[1][bit].append(str(nbFeatTot))\n else:\n arr[1][bit].append(ids_now[i] )\n if ids_now[i] not in subm_all:\n nbFeatTot+=1\n subm_all.append(ids_now[i] )\n fps_by_comp[0][molecule_nb].append(ids_now[i] )\n arr2[1][bit].append(str(nbFeatTot))\n\n\n else: #The bit is already on!\n env=Chem.FindAtomEnvironmentOfRadiusN(m,radius,atom)\n amap={}\n submol=Chem.PathToSubmol(m,env,atomMap=amap)\n\n if len(amap)==0 and ids_now[i] not in arr[1][bit]:\n arr[1][bit].append(ids_now[i] )\n \n if ids_now[i] not in subm_all:\n nbFeatTot+=1\n subm_all.append(ids_now[i] )\n fps_by_comp[0][molecule_nb].append(ids_now[i] )\n arr2[1][bit].append(str(nbFeatTot))\n # We keep the all the features for each compound anyway\n if len(amap)==0 and ids_now[i] in arr[1][bit]:\n fps_by_comp[0][molecule_nb].append(ids_now[i] )\n\n if len(amap)!=0 and ids_now[i] not in arr[1][bit]:\n arr[1][bit].append(ids_now[i] )\n \n if ids_now[i] not in subm_all:\n nbFeatTot+=1\n subm_all.append(ids_now[i] )\n fps_by_comp[0][molecule_nb].append(ids_now[i] )\n arr2[1][bit].append(str(nbFeatTot))\n # We keep the all the features for each compound anyway\n if len(amap)!=0 and ids_now[i] in arr[1][bit]:\n fps_by_comp[0][molecule_nb].append(ids_now[i] )\n",
"cell_type": "code",
"prompt_number": 253,
"outputs": [],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Define a function to create matrices of empty strings\n"
},
{
"metadata": {},
"input": "def nans(shape, dtype=str):\n a = np.empty(shape, dtype)\n a[:]=\"\"\n return a\n\n# Now we calculate the unhashed fingerprints with and without counts:\nFPS=nans((nbMols,len(subm_all)))\nFPS_counts=nans((nbMols,len(subm_all)))\n\nfor i in range(nbMols):\n for j in range(len(subm_all)):\n if subm_all[j] in fps_by_comp[0][i]:\n FPS[i][j]=1\n FPS_counts[i][j]=fps_by_comp[0][i].count(subm_all[j])\n else:\n FPS[i][j]=0\n FPS_counts[i][j]=0",
"cell_type": "code",
"prompt_number": 254,
"outputs": [],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "###Calculating Circular Morgan Fingerprints with the FingerprintCalculator.py.\nThis is suitable when you want to use the fps in other programming languages (e.g. the R caret package)\nFor help on how to use it:"
},
{
"metadata": {},
"input": "%run FingerprintCalculator.py --help",
"cell_type": "code",
"prompt_number": 255,
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "usage: PROG [-h] --bits BITS --rad RAD --f F [--mols MOLS] [--image]\n [--unhashed] [--v] [--extF EXTF] [--molsEXT MOLSEXT]\n [--unhashedEXT] --RDkitPath RDKITPATH --output OUTPUT\n\nGet Morgan Fingerprints for compounds codified in either SMILES or SDF format\nusing RDkit. Isidro Cortes Ciriano. August/September 2013\n\noptional arguments:\n -h, --help show this help message and exit\n --bits BITS Size of the hashed Morgan Fingerprints (binary and\n with counts)\n --rad RAD Maximum diameter. Deafault is two, equivalent to ECFP4\n from PipelinePilot\n --f F Format of the input file\n --mols MOLS File containing the molecules {.smi|.smiles|.sdf}. If\n the format is smiles, each line should contain the\n smiles and the name separated by a comma (in this\n order)\n --image Write --image if you want the images of the\n substructures\n --unhashed Write --unhashed if you want the unhashed fingerprints\n --v Verbose\n --extF EXTF Type -extF followed by the format {.smi|.smiles|.sdf}\n of the external file for which you want to calculate\n HASHED circular fingerprints\n --molsEXT MOLSEXT External file\n --unhashedEXT Write --unhashedEXT if you want the unhashed\n fingerprints for the external file. The substructures\n of the molecules in the external file will be compared\n to the pool of substructures contained in the\n molecules of the main file\n --RDkitPath RDKITPATH\n Path to the directory where the RDkit files are\n --output OUTPUT Name of the output files\n"
}
],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"input": "%run ./FingerprintCalculator.py --bits 256 --rad 2 --f smi --mols example_set.smi --image --unhashed --RDkitPath $RDbase --output test_fpcalc_",
"cell_type": "code",
"prompt_number": 5,
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "Erroneous input at molecule: 2\n"
}
],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Section 3. Machine Learning with scikit learn\n"
},
{
"metadata": {},
"input": "from sklearn import svm, grid_search,cross_validation\nfrom sklearn.cross_validation import StratifiedKFold\nfrom sklearn.svm import SVR as SVR\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn import preprocessing\nimport pylab as pl",
"cell_type": "code",
"prompt_number": 259,
"outputs": [],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Recall:\n\nWe have the following descriptors / fps calculated:\n\n- Desc_values : physicochemical descriptors\n- Morgan_fps : binary hashed Morgan fps\n- FPS / FPS_counts: Unhashed Morgan fps {binary and with counts}"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Training a SVM\n\n#### 3.1. With physichochemical compound descriptors\n"
},
{
"metadata": {},
"input": "# Divide the original dataset into training and tesst set\nX_train, X_test, y_train, y_test = cross_validation.train_test_split(Desc_values, logS_correct, test_size=0.3, random_state=23)\nX_test = X_test.astype(float)\nX_train = X_train.astype(float)\n\n# Center and scale the data\n# more about preprocessing here: http://scikit-learn.org/stable/modules/preprocessing.html\n# If imputation were required:\n#from sklearn.preprocessing import Imputer\n#X = sp.csc_matrix([[1, 2], [np.nan, 3], [7, 6]])\n#imp = Imputer(missing_values='NaN', strategy='mean', axis=0)\n#imp.fit(X)\n#imp.transform(X)\n\nX_train = preprocessing.scale(X_train)\nX_test = preprocessing.scale(X_test)\n\n## Grid for the CV\ngammas = np.logspace(-10,4,8,base=2)\nCost = [0.001,0.001,0.01,1,10,100,1000]\nparam_grid = [\n {'C': Cost , 'gamma': gammas, 'kernel': ['rbf']},\n ]\n\ncv_stratified = StratifiedKFold(y_train, n_folds=5)\n\n# It prints the composition of each fold (indices)\n#for train, test in cv:\n# print(\"%s %s\" % (train, test))\n\nclf = grid_search.GridSearchCV(estimator=SVR(), param_grid=param_grid, cv=cv_stratified,scoring='mean_squared_error') # or r2\nmodel = clf.fit(X_train,y_train) ",
"cell_type": "code",
"prompt_number": 1,
"outputs": [
{
"ename": "NameError",
"evalue": "name 'cross_validation' is not defined",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-74e128ad8c10>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;31m# Divide the original dataset into training and tesst set\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_validation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDesc_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogS_correct\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m23\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0mX_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mX_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'cross_validation' is not defined"
]
}
],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"input": "from sklearn.metrics import mean_squared_error\n\nztest = model.predict(X_test)\nmean_squared_error(y_test, ztest)\n\nztest_train = model.predict(X_train)\n\nfigure,(plt1,plt2) = pylab.subplots(1,2)\nfigure.set_size_inches(15,5)\nplt1.scatter(y_test,ztest,c='green',label='test')\nplt1.set_title(\"Prediction test set\")\nminn = np.min(np.concatenate([y_test,ztest],axis=0))\nmaxx = np.max(np.concatenate([y_test,ztest],axis=0))\nplt1.set_ylim([minn,maxx])\nplt1.set_xlim([minn,maxx])\nplt2.scatter(y_train,ztest_train,c='green',label='test')\nplt2.set_title(\"Prediction training set\")\nminn = np.min(np.concatenate([y_train,ztest_train],axis=0))\nmaxx = np.max(np.concatenate([y_train,ztest_train],axis=0))\nplt2.set_ylim([minn,maxx])\nplt2.set_xlim([minn,maxx])",
"cell_type": "code",
"prompt_number": 301,
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 301,
"text": "(-11.619999999999999, 1.5085022433773303)"
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAA2QAAAFCCAYAAACegh7KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDvJ/mqq+tPbSPLsAAAAASUVORK5CYII=\n",
"text": "<matplotlib.figure.Figure at 0x111c97b50>"
}
],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Definition of a function to remove near zero variance descriptors:"
},
{
"metadata": {},
"input": "from collections import defaultdict\n\ndef rmNearZeroVarDescs(descs,ratio):\n colNb = descs.shape[1]\n to_keep=[]\n for j in range(0,colNb):\n descs_now = descs[:,j]\n d = defaultdict(int)\n for i in descs_now:\n d[i] += 1\n freqs=sorted(d.iteritems(), key=lambda x: x[1], reverse=True)\n if len(freqs) > 1: # there is more than one value..\n most_frequent1 = freqs[0][1]\n most_frequent2 = freqs[1][1]\n if (np.array(most_frequent1)/np.array(most_frequent2)) < ratio:\n to_keep.append(j)\n return np.array(to_keep)\n ",
"cell_type": "code",
"prompt_number": 279,
"outputs": [],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### 3.2. With Hashed Morgan FPs as compound descriptors\n"
},
{
"metadata": {},
"input": "# Divide the original dataset into training and tesst set\nX_train, X_test, y_train, y_test = cross_validation.train_test_split(Morgan_fps, logS_correct, test_size=0.3, random_state=23)\nX_test = X_test.astype(float)\nX_train = X_train.astype(float)\n\nX_train = preprocessing.scale(X_train)\nX_test = preprocessing.scale(X_test)\n\n\n## Grid search\ngammas = np.logspace(-10,4,8,base=2)\nCost = [0.001,0.001,0.01,1,10,100,1000]\nparam_grid = [\n {'C': Cost , 'gamma': gammas, 'kernel': ['rbf']},\n ]\n\ncv_stratified = StratifiedKFold(y_train, n_folds=5)\n\n# It prints the composition of each fold (indices)\n#for train, test in cv:\n# print(\"%s %s\" % (train, test))\n\nclf_fps = grid_search.GridSearchCV(estimator=SVR(), param_grid=param_grid, cv=cv_stratified,scoring='mean_squared_error') # or r\nmodel_fps = clf_fps.fit(X_train,y_train) \n",
"cell_type": "code",
"prompt_number": 327,
"outputs": [],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"input": "from sklearn.metrics import mean_squared_error\n\nztest = model_fps.predict(X_test)\nmean_squared_error(y_test, ztest)\n\nfigure,(plt1,plt2) = pylab.subplots(1,2)\nfigure.set_size_inches(15,5)\nplt1.scatter(y_test,ztest,c='green',label='test')\nminn = np.min(np.concatenate([y_test,ztest],axis=0))\nmaxx = np.max(np.concatenate([y_test,ztest],axis=0))\nplt1.set_ylim([minn,maxx])\nplt1.set_xlim([minn,maxx])\nplt1.set_title(\"Prediction test set\")\nplt2.hist(y_test)\nplt2.set_title(\"Distribution Response variable (logS)\")",
"cell_type": "code",
"prompt_number": 328,
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 328,
"text": "<matplotlib.text.Text at 0x111c94290>"
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAA2YAAAFCCAYAAACad873AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nREREREQMU2MmIiIiIiJi2P8DI9Nn7Eh5fGQAAAAASUVORK5CYII=\n",
"text": "<matplotlib.figure.Figure at 0x10fe81190>"
}
],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"input": "####\n# 3.3. With UN-Hashed Morgan FPs as compound descriptors\n####\n# Divide the original dataset into training and tesst set\nX_train, X_test, y_train, y_test = cross_validation.train_test_split(FPS, logS_correct, test_size=0.3, random_state=23)\nX_test = X_test.astype(float)\nX_train = X_train.astype(float)\n\nX_train = preprocessing.scale(X_train)\nX_test = preprocessing.scale(X_test)\n\n# Remove near zero variance descriptors\nto_keep=rmNearZeroVarDescs(FPS,30/1)\nX_train=X_train[:,to_keep]\nX_test=X_test[:,to_keep]\n\n## Grid search\ngammas = np.logspace(-10,4,8,base=2)\nCost = [0.001,0.001,0.01,1,10,100,1000]\nparam_grid = [\n {'C': Cost , 'gamma': gammas, 'kernel': ['rbf']},\n ]\n\ncv_stratified = StratifiedKFold(y_train, n_folds=5)\n\nclf_fps = grid_search.GridSearchCV(estimator=SVR(), param_grid=param_grid, cv=cv_stratified,scoring='mean_squared_error') # or r\nmodel_fps = clf_fps.fit(X_train,y_train) ",
"cell_type": "code",
"prompt_number": 295,
"outputs": [],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"input": "ztest = model_fps.predict(X_test)\nztest_train = model_fps.predict(X_train)\n\nfigure,(plt1,plt2) = pylab.subplots(1,2)\nfigure.set_size_inches(15,5)\nplt1.scatter(y_test,ztest,c='green',label='test')\nminn = np.min(np.concatenate([y_test,ztest],axis=0))\nmaxx = np.max(np.concatenate([y_test,ztest],axis=0))\nplt1.set_ylim([minn,maxx])\nplt1.set_xlim([minn,maxx])\nplt1.set_title(\"Prediction test set (UNHASHED)\")\n\nplt2.scatter(y_train,ztest_train,c='green',label='test')\nplt2.set_title(\"Prediction training set (UNHASHED)\")\nminn = np.min(np.concatenate([y_train,ztest_train],axis=0))\nmaxx = np.max(np.concatenate([y_train,ztest_train],axis=0))\nplt2.set_ylim([minn,maxx])\nplt2.set_xlim([minn,maxx])",
"cell_type": "code",
"prompt_number": 296,
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 296,
"text": "(-11.619999999999999, 1.3400000000000001)"
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAA2QAAAFCCAYAAACegh7KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text": "<matplotlib.figure.Figure at 0x10f05c210>"
}
],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### 3.4. With UN-Hashed Morgan FPs as compound descriptors training Trees\n"
},
{
"metadata": {},
"input": "from sklearn import tree\nfrom sklearn.cross_validation import KFold\nfrom sklearn.grid_search import GridSearchCV\n\ncustom_tree = tree.DecisionTreeRegressor(compute_importances=True)\n\n\nparams = {'max_depth':[2,5,10,20,200],'min_samples_split':[2,8,32,128,256,512,1024],'min_samples_leaf':[1,2,5,10,100,500,1000],'compute_importances':[True]}\n\ncv = KFold(n=len(X_train),k=10,indices=False,shuffle=True)\ncv_stratified = StratifiedKFold(y_train, n_folds=5)\ngs = GridSearchCV(custom_tree, params, cv=cv_stratified,verbose=1,refit=True)\ngs.fit(X_train,y_train)\n\nztest = gs.predict(X_test)\nztest_train = gs.predict(X_train)\n\nfigure,(plt1,plt2) = pylab.subplots(1,2)\nfigure.set_size_inches(15,5)\nplt1.scatter(y_test,ztest,c='green',label='test')\nminn = np.min(np.concatenate([y_test,ztest],axis=0))\nmaxx = np.max(np.concatenate([y_test,ztest],axis=0))\nplt1.set_ylim([minn,maxx])\nplt1.set_xlim([minn,maxx])\nplt1.set_title(\"Prediction test set (UNHASHED)\")\n\nplt2.scatter(y_train,ztest_train,c='green',label='test')\nplt2.set_title(\"Prediction training set (UNHASHED)\")\nminn = np.min(np.concatenate([y_train,ztest_train],axis=0))\nmaxx = np.max(np.concatenate([y_train,ztest_train],axis=0))\nplt2.set_ylim([minn,maxx])\nplt2.set_xlim([minn,maxx])",
"cell_type": "code",
"prompt_number": 324,
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": "/Library/Python/2.7/site-packages/scikit_learn-0.14.1-py2.7-macosx-10.8-intel.egg/sklearn/tree/tree.py:632: DeprecationWarning: Setting compute_importances is no longer required as version 0.14. Variable importances are now computed on the fly when accessing the feature_importances_ attribute. This parameter will be removed in 0.16.\n DeprecationWarning)\n/Library/Python/2.7/site-packages/scikit_learn-0.14.1-py2.7-macosx-10.8-intel.egg/sklearn/cross_validation.py:240: DeprecationWarning: The parameter k was renamed to n_folds and will be removed in 0.15.\n \" removed in 0.15.\", DeprecationWarning)\n[Parallel(n_jobs=1)]: Done 1 jobs | elapsed: 0.0s\n[Parallel(n_jobs=1)]: Done 50 jobs | elapsed: 0.0s\n[Parallel(n_jobs=1)]: Done 200 jobs | elapsed: 0.1s\n"
},
{
"output_type": "stream",
"stream": "stderr",
"text": "[Parallel(n_jobs=1)]: Done 450 jobs | elapsed: 0.3s\n[Parallel(n_jobs=1)]: Done 800 jobs | elapsed: 0.5s\n"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "Fitting 5 folds for each of 245 candidates, totalling 1225 fits\n"
},
{
"output_type": "stream",
"stream": "stderr",
"text": "[Parallel(n_jobs=1)]: Done 1225 out of 1225 | elapsed: 0.8s finished\n"
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 324,
"text": "(2.0, 5.7999999999999998)"
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAA2YAAAFCCAYAAACad873AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
"text": "<matplotlib.figure.Figure at 0x11148b810>"
}
],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"input": "def createImportancePlot(splt,desc,importances,caption):\n import numpy.numarray as na \n labels = []\n weights = []\n threshold = sort([abs(w) for w in importances])[-11]\n for d in zip(desc,importances):\n if abs(d[1]) > threshold:\n labels.append(d[0])\n weights.append(d[1])\n xlocations = na.array(range(len(labels)))+0.5\n width = 0.8\n splt.bar(xlocations, weights, width=width)\n splt.set_xticks([r+1 for r in range(len(labels))])\n splt.set_xticklabels(labels)\n splt.set_xlim(0, xlocations[-1]+width*2)\n splt.set_title(caption)\n splt.get_xaxis().tick_bottom()\n splt.get_yaxis().tick_left()",
"cell_type": "code",
"prompt_number": 298,
"outputs": [],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"input": "best_treemodel = gs.best_estimator_\nfeat_imp = best_treemodel.feature_importances_\nprint len(feat_imp)\nfig,a = pyplot.subplots(1,1)\nfig.set_size_inches(15,5)\ncreateImportancePlot(a,range(1,X_test.shape[1]),feat_imp,\"Most important descriptors in the optimized tree\")",
"cell_type": "code",
"prompt_number": 299,
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "96\n"
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAA2wAAAFCCAYAAACNVV4+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3X1YVHX+//HXQcZuDBUQURhoVBBGSyVRs9aiUqi+30zN\nXNSsjIzcte3uct22baU779q2Ur72JSuzby26bV1iZdSqYa6bTaZZm1rqigx4k3mTgreMn98f/JwV\ngRFv5yDPx3VxXZw5n3PmfT5z5nBenDOfsYwxRgAAAAAA2wkJdgEAAAAAgNoR2AAAAADApghsAAAA\nAGBTBDYAAAAAsCkCGwAAAADYFIENAAAAAGyKwAYAp+Htt99WRkZGsMuwjbvvvltPPPHEOX3OsLAw\nFRcXn9PnrI+bb75Z//d//3dG1lVcXKyQkBAdOXLkjKzvTBo9erSeeeaZU1p24sSJGjVq1Bmtp6io\nSHFxcWd0nQAQTAQ2AA2ay+XSBRdcoB07dlR7PCUlRSEhISopKTmt9YeEhOjf//53nfOHDx+ujz/+\n+LSe40x544031KdPnzO2vpycHI0YMeKklrEsS5ZlnbEa6mPv3r1yuVz1anui1/NMmj9//kn331Eu\nl0uLFi06wxWdvtr2sZdffll/+MMfTml9jz32mGbMmHEmSqu3c7kPAMCZQGAD0KBZlqX27dsrPz/f\n/9i3336r/fv3n7HgYIw5I+s5myorK4Ndgt+56q9T3eZTre9c9rFlWQ1iv2uoAvWtnd5LACAR2ACc\nB+644w69+eab/ulZs2bpzjvvrHZS9vPPP+vOO+9U69at5XK59Oyzz/rnr1+/Xtdee61atmypqKgo\nDR06VJJ0zTXXSJK6du2qsLAwvfPOOzWe+/grDiEhIXr55ZeVmJio5s2b649//KM2bNig3r17q2XL\nlsrMzNThw4clVd265XQ6NXHiREVFRaldu3b6y1/+Uq+a33jjDV199dV65JFH1KpVK2VmZmr06NH6\n/PPPFRYWpoiICEnShx9+qJSUFLVo0ULx8fF68skn/es/epvdm2++qUsvvVRRUVGaMGGCJKmwsFAT\nJ07UnDlzFBYWppSUlFr7fuXKlbriiivUvHlzZWZm6sCBA9Xmf/DBB+rWrZvCw8N19dVX69tvv/XP\nmzx5spxOp5o3b67k5GT/FSWfz6cJEyYoISFBzZs3V2pqqsrKyvz9O336dCUmJiopKcn/2NErJnff\nfbfuv/9+paenq3nz5kpLS/NfZa3r9ZwxY4YSExMVGRmpW2+9VVu2bKn2eh7/fA8//LCio6PVokUL\ndenSRd99912tfZOWlqbXXnvN/3r94he/0NixYxUREaH27dursLCw1uVGjBihkpIS3XLLLQoLC9Of\n/vQn/7y33nqrxmslVQWQSZMmKSEhQa1atdIvf/lL7dq1q9b112ebp02bpg4dOigqKkq//e1vZYzR\nmjVrat3Hjr0N9ug+/dxzz6l169aKiYnR3LlzNX/+fHXs2FGRkZGaNGmS/7mOvYo7ZswYhYWF+X8c\nDod/f928ebNuu+02tW7dWu3bt9e0adP869i/f7/uvvtuRUREqHPnzvryyy/r3O7a9oGjNU+ZMkVt\n27ZVVlbWCftz2bJluuqqqxQeHq5u3bpp8eLFdT4nAJw2AwANmMvlMgsWLDBJSUlmzZo1prKy0jid\nTrNp0yZjWZbZtGmTMcaYESNGmAEDBpjy8nJTXFxsOnbsaF577TVjjDGZmZlmwoQJxhhjDh48aJYu\nXepfv2VZZsOGDXU+/8yZM80vfvGLau0HDBhg9u7da7777jvTtGlTc91115mNGzean3/+2XTq1MnM\nmjXLGGPMp59+akJDQ82jjz5qDh06ZBYvXmyaNWtmvv/++xPWPHPmTBMaGmpyc3ONz+cz+/fvN2+8\n8Ua1WowxpqioyPzrX/8yxhjzzTffmOjoaDN37lxjjDEbN240lmWZ++67zxw4cMCsWrXKXHDBBWbt\n2rXGGGNycnLMiBEj6tz2gwcPmvj4ePPiiy+ayspK87e//c04HA7zxBNPGGOMWbFihWndurXxeDzm\nyJEjZtasWcblcplDhw6ZtWvXmri4OLNlyxZjjDGbNm3y9/OUKVPM5Zdfbn744QdjjDGrVq0yO3bs\n8Pdvenq62bVrlzlw4ECN1+iuu+4yYWFhZsmSJebgwYPmwQcfrPH6HPt6Lly40LRq1cqsXLnSHDx4\n0DzwwAPmmmuuqdb+2OcrLCw03bt3Nz///LMxxpi1a9f6t+F4aWlp1V4vh8NhXn31VXPkyBHz8ssv\nm5iYmDr71uVymYULF/qnT/Ravfjii6Z3796mrKzMHDp0yGRnZ5uhQ4fWuu76bPP1119vdu3aZUpK\nSkzHjh3Nq6++aowxte5jd999t/81P7pPP/3006aystLMmDHDREZGmmHDhpny8nLz3XffmYsuusgU\nFxcbY6r2sTvuuKNGjStXrjRRUVHm66+/Nj6fz1xxxRXm6aefNocPHzb//ve/Tfv27c3HH39sjDFm\n3Lhx5pprrjG7du0yXq/XdO7c2cTFxdXZt8fvA0dr/t3vfmcOHTpk9u/fH7A/S0tLTWRkpPnoo4+M\nMcb8/e9/N5GRkWb79u11PicAnA4CG4AG7Whge+aZZ8xjjz1mPvroI5Oenm4qKyv9ga2ystI0bdrU\nrFmzxr9cXl6eSUtLM8YYc+edd5r77rvPlJaW1lj/qQS2f/7zn/7p7t27mylTpvinH330UfPQQw8Z\nY/5zorhv3z7//CFDhvhPdgPVPHPmTBMfHx+wlto8+OCD5uGHHzbG/CcElJWV+ef37NnTzJkzxxhj\nzPjx42s9mT5q8eLFNULHVVdd5T95v//++/2/H5WUlGQWL15s1q9fb1q3bm0WLFhgDh06VKPNvHnz\nan1Oy7LMp59+WuOxYwPbsUGlvLzcNGnSxP/aHv963nPPPWbcuHHV2jscDn/QP/75Fi1aZDp27GiW\nLVtmfD5fnX1jTM3AlpCQ4J9XUVFhLMsy27Ztq3XZugJbXa9VcnJytfabN282Doej1hrrs81Hw5Ax\nxkyfPt3ccMMN/u2oLbD94Q9/MMZU7dMXXXSROXLkiDHGmD179hjLsozH4/G37969uykoKDDG1L6P\n/fjjj+bSSy/1b9uyZctq7OsTJkwwI0eONMaYauHNGGNeeeUV43Q6a2z3UbUFtqZNm5qDBw/6H3O7\n3bX2Z2VlpZk0aVKNf2RkZGT4/xEDAGcat0QCaPAsy9KIESP09ttv13o75E8//aTDhw/r0ksv9T8W\nHx/vv81uypQpMsaoZ8+euuyyyzRz5szTqic6Otr/+0UXXVRt+sILL1R5ebl/Ojw8XBdddJF/+tJL\nL9WWLVu0Y8eOgDVLqtdIeF988YWuu+46tW7dWi1btlReXl6NAVratGnj//3iiy+uVl8gmzdvVmxs\nbLXHjq1306ZNev755xUeHu7/KS0t1ZYtW9ShQwe9+OKLysnJUXR0tIYOHeq/Lc/r9apDhw51Pm+g\n7bYsS06n0z/drFkzRUREaPPmzbW237JlS7WamzVrpsjIyDr7+brrrtOYMWP061//WtHR0crOztbe\nvXvrrOdYx/ezpHr3dV3rOLr8pk2bNHDgQH8/d+rUSaGhodq2bVuNdZzsNsfHx9fZf7WJjIz0f370\n6L59/Huiru0+fPiwBg8erDvuuENDhgzxb9vmzZur7UcTJ07Ujz/+KKlqPzy+3pMVFRWlpk2b+qeL\ni4vr7M9NmzbpnXfeqVbP0qVLtXXr1pN+XgCoDwIbgPNCfHy82rdvr48++kiDBg2qNq9Vq1ZyOBzV\nhn4vKSnxn9hHR0frlVdeUVlZmfLy8vSrX/3qrI0id/xAKLt27dK+ffv805s2bVJMTMwJa65tXbUN\nsjJs2DANGDBApaWl2r17t+6///56Dw1/okFb2rZtW+0k/2j9R8XHx+vxxx/Xrl27/D/l5eX65S9/\nKUkaOnSolixZok2bNsmyLI0bN05SVVhYv379KdVljJHX6/VPl5eXa+fOnYqJiam1fUxMTLU+rqio\n0I4dO6oF0eOf74EHHtDy5cu1evVq/fDDD3ruuefqrOdUneyAOfHx8SosLKzW1/v27VPbtm1rtK3P\nNh87umpJSYl/Xl11nakBfh544AG1bNmy2tcExMfHq127dtW2bc+ePfrggw8kVe2Hx9d7so6vv67+\njImJUXx8vEaMGFFt3t69e/Xb3/72FLcaAAIjsAE4b7z22mtatGhRtStWktSkSRMNGTJEjz/+uMrL\ny7Vp0ya98MILuuOOOyRJ77zzjkpLSyVJLVu2lGVZCgmpOjxGR0drw4YNp1XXsVf7TC2j040fP16H\nDx/WkiVL9OGHH+r2229XSEhIwJpr06ZNG5WWlvoHNZGqAkt4eLiaNm0qj8ejv/zlL/U+uW7Tpo2K\ni4vrHFHvqquuUmhoqKZOnarDhw/rvffeqzbgw6hRo/S///u/8ng8MsaooqJCH374ocrLy/XDDz9o\n0aJFOnjwoC644AJdeOGFatKkiSTp3nvv1RNPPKH169fLGKNvvvlGO3furFfNUtVw+kuXLtWhQ4f0\nxBNPqHfv3v7AcfzrOXToUM2cOVOrVq3SwYMH9fvf/15XXnllnVdpli9fri+++EKHDx/WxRdfXK3u\nM+lk97v7779fv//97/1hZfv27Zo3b16tbeuzzX/605+0e/dueb1eTZ061R+yo6Oja+xjpurjFaey\nmdXk5eXps88+01tvvVXt8Z49eyosLExTpkzR/v375fP59K9//UvLly+XJA0ZMkQTJ07U7t27VVpa\nWm1AktrUp28D9ecdd9yh999/X5988ol8Pp8OHDigoqKiGv+8AIAzhcAG4LzRvn17XXHFFf7pY4PJ\ntGnT1KxZM7Vv3159+vTR8OHDdc8990iqOgm/8sorFRYWpltvvVVTp071f69XTk6O7rrrLoWHh+tv\nf/tbjec8/nvHagtDx88/drpNmzYKDw9XTEyMRowYoby8PHXs2LHOmkeOHFnreiTp+uuvV+fOndWm\nTRu1bt1akjR9+nT98Y9/VPPmzfX000/7T7wD1XvU7bffLqnqFrfU1NQa8x0Oh9577z298cYbioyM\n1F//+lfddttt/vndu3fXjBkzNGbMGEVERCgxMdE/mufBgwf12GOPKSoqSm3bttVPP/2kiRMnSpIe\neeQRDRkyROnp6WrRooVGjRrlH32yPv07bNgwPfnkk4qMjNTKlSurBYDjX88bbrhBTz/9tG677TbF\nxMRo48aNmj17dp39s2fPHt13332KiIiQy+VSq1atNHbs2Dr78Nj11OeK6FGPPfaYnnnmGYWHh+vP\nf/7zCds/+OCD6t+/v390zN69e8vj8dTa9kTbLEm33nqrunfvrpSUFP33f/+3/71yww031NjHTvQe\nCFT3scvOnj1bGzduVExMjH+kyEmTJikkJEQffPCBvv76a7Vv315RUVG67777tGfPHklV//C49NJL\n1a5dO91444268847Az7n8ftAba9NoP50Op0qKCjQhAkT1Lp1a8XHx+v555+35ZeaAzg/WOYE/xYr\nLCzUQw89JJ/Pp3vvvdd/y8rxvvzyS/Xu3Vtz5szx/8F2uVxq3ry5mjRpIofDUecfDwBojIqKijRi\nxIhqt/Dh9IwcOVJOp1NPP/10sEtpsEJCQrR+/Xq1b98+2KUAACSFBprp8/k0ZswYLViwQLGxserR\no4f69+8vt9tdo924ceN04403VnvcsiwVFRX5v6sFAICz6UzcmgcAgJ0EvCXS4/EoISFBLpdLDodD\nmZmZKigoqNFu2rRpGjx4sKKiomrM448nANTtTA3WgCq13d6Gk0P/AYC9BLzCVlZWVm2oXKfTqS++\n+KJGm4KCAi1atEhffvlljfvY+/btqyZNmig7O1ujRo06w+UDQMOVlpZ2SiPaoW6n+5UMqLprBgBg\nHwEDW33+y/bQQw9p0qRJsiyrxkhRS5cuVdu2bbV9+3b169dPycnJ6tOnz+lXDQAAAACNQMDAFhsb\nW+3D8F6vt9p3AEnSV199pczMTElVX0770UcfyeFwqH///v7vf4mKitLAgQPl8XhqBLZu3bpp1apV\nZ2RjAAAAAKChufbaa1VUVFTrvICjRFZWViopKUkLFy5UTEyMevbsqfz8/BqDjhw1cuRI3XLLLRo0\naJD27dsnn8+nsLAwVVRUKD09XePHj1d6enr1Av7/lTkAAAAAaIwCZaKAV9hCQ0OVm5urjIwM+Xw+\nZWVlye12Ky8vT5KUnZ1d57Jbt27VoEGDJFUFv+HDh9cIawAAAACAup3we9jOegFcYQMAAADQiAXK\nRAGH9QcAAAAABA+BDQAAAABsisAGAAAAADZFYAMAAAAAmwo4SiQAAGjcmjeP0N69u4JdxjkVFhau\nPXt2BrsMAJDEKJEAACAAy7IkNba/05ybADi3GCUSAAAAABogAhsAAAAA2BSBDQAAAABsisAGAAAA\nADZFYAMAAAAAmyKwAQAAAIBNEdgAAAAAwKYIbAAAAABgUwQ2AAAAALApAhsAAAAA2BSBDQAAAABs\nisAGAAAAADZFYAMAAAAAmyKwAQAAAIBNEdgAAAAAwKYIbAAAAABgUycMbIWFhUpOTlZiYqImT55c\nZ7svv/xSoaGhevfdd096WQAAAABATQEDm8/n05gxY1RYWKjVq1crPz9fa9asqbXduHHjdOONN570\nsgAAAACA2gUMbB6PRwkJCXK5XHI4HMrMzFRBQUGNdtOmTdPgwYMVFRV10ssCAAAAAGoXMLCVlZUp\nLi7OP+10OlVWVlajTUFBgUaPHi1Jsiyr3ssCAAAAAOoWMLAdDV+BPPTQQ5o0aZIsy5IxRsaYei8L\nAAAAAKhbaKCZsbGx8nq9/mmv1yun01mtzVdffaXMzExJ0k8//aSPPvpIDoejXsselZOT4/89LS1N\naWlpJ7sdAAAAAHDesczRS2K1qKysVFJSkhYuXKiYmBj17NlT+fn5crvdtbYfOXKkbrnlFg0aNKje\nyx69MgcAAOyn6o6ZxvZ3mnMTAOdWoEwU8ApbaGiocnNzlZGRIZ/Pp6ysLLndbuXl5UmSsrOzT3pZ\nAAAAAED9BLzCdk4K4AobAAC2xRU2ADj7AmWiE35xNgAAAAAgOAhsAAAAAGBTBDYAAAAAsCkCGwAA\nAADYFIENAAAAAGyKwAYAAAAANkVgAwAAAACbIrABAAAAgE0R2AAAAADApghsAAAAAGBTBDYAAAAA\nsCkCGwAAAADYFIENAAAAAGyKwAYAAAAANkVgAwAAAACbIrABAAAAgE0R2AAAAADApghsAAAAAGBT\nBDYAAAAAsCkCGwAAAADYFIENAAAAAGyKwAYAAAAANnXCwFZYWKjk5GQlJiZq8uTJNeYXFBSoa9eu\nSklJUffu3bVo0SL/PJfLpS5duiglJUU9e/Y8s5UDAAAAwHnOMsaYumb6fD4lJSVpwYIFio2NVY8e\nPZSfny+32+1vU1FRoWbNmkmSvv32Ww0cOFDr16+XJLVr105fffWVIiIi6i7AshSgBAAAEESWZUlq\nbH+nOTcBcG4FykQBr7B5PB4lJCTI5XLJ4XAoMzNTBQUF1docDWuSVF5erlatWlWbzwEPAAAAAE5N\nwMBWVlamuLg4/7TT6VRZWVmNdnPnzpXb7dZNN92kqVOn+h+3LEt9+/ZVamqqZsyYcQbLBgAAAIDz\nX2igmVW3QZzYgAEDNGDAAC1ZskQjRozQ999/L0launSp2rZtq+3bt6tfv35KTk5Wnz59Tr9qAAAA\nAGgEAga22NhYeb1e/7TX65XT6ayzfZ8+fVRZWakdO3YoMjJSbdu2lSRFRUVp4MCB8ng8tQa2nJwc\n/+9paWlKS0s7yc0AAAAAgPNPwMCWmpqqdevWqbi4WDExMZozZ47y8/OrtdmwYYPat28vy7K0YsUK\nSVJkZKT27dsnn8+nsLAwVVRU6JNPPtH48eNrfZ5jAxsAAAAAoErAwBYaGqrc3FxlZGTI5/MpKytL\nbrdbeXl5kqTs7Gy9++67evPNN+VwOHTJJZdo9uzZkqStW7dq0KBBkqTKykoNHz5c6enpZ3lzAAAA\nAOD8EXBY/3NSAMP6AwBgWwzrDwBn3ykP6w8AAAAACB4CGwAAAADYFIENAAAAAGyKwAYAAAAANkVg\nAwAAAACbIrABAAAAgE0R2AAAAADApghsAAAAAGBTBDYAAAAAsCkCGwAAAADYFIENAAAAAGyKwAYA\nAAAANkVgAwAAAACbIrABAAAAgE0R2AAAAADApghsAAAAAGBTBDYAAAAAsCkCGwAAAADYFIENAAAA\nAGyKwAYAAAAANkVgAwAAAACbIrABAAAAgE2dMLAVFhYqOTlZiYmJmjx5co35BQUF6tq1q1JSUtS9\ne3ctWrSo3ssCAAAAAOpmGWNMXTN9Pp+SkpK0YMECxcbGqkePHsrPz5fb7fa3qaioULNmzSRJ3377\nrQYOHKj169fXa1lJsixLAUoAAABBZFmWpMb2d5pzEwDnVqBMFPAKm8fjUUJCglwulxwOhzIzM1VQ\nUFCtzdGwJknl5eVq1apVvZcFAAAAANQtYGArKytTXFycf9rpdKqsrKxGu7lz58rtduumm27S1KlT\nT2pZAAAAAEDtAga2qtsgTmzAgAFas2aN3n//fY0YMYLbCAAAAADgDAgNNDM2NlZer9c/7fV65XQ6\n62zfp08fVVZWaufOnXI6nfVeNicnx/97Wlqa0tLS6lk+AAAAAJy/Aga21NRUrVu3TsXFxYqJidGc\nOXOUn59frc2GDRvUvn17WZalFStWSJIiIyPVokWLEy571LGBDQAAAABQJWBgCw0NVW5urjIyMuTz\n+ZSVlSW32628vDxJUnZ2tt599129+eabcjgcuuSSSzR79uyAywIAAAAA6ifgsP7npACG9QcAwLYY\n1h8Azr5THtYfAAAAABA8BDYAAAAAsCkCGwAAAADYFIENAAAAAGyKwAYAAAAANkVgAwAAAACbIrAB\nAAAAgE0R2AAAAADApghsAAAAAGBTBDYAAAAAsCkCGwAAAADYFIENAAAAAGyKwAYAAAAANkVgAwAA\nAACbIrABAAAAgE0R2AAAAADApghsAAAAAGBTBDYAAAAAsCkCGwAAAADYFIENAAAAAGyKwAYAAAAA\nNkVgAwAAAACbOmFgKywsVHJyshITEzV58uQa899++2117dpVXbp00dVXX61vvvnGP8/lcqlLly5K\nSUlRz549z2zlAAAAAHCes4wxpq6ZPp9PSUlJWrBggWJjY9WjRw/l5+fL7Xb723z++efq1KmTWrRo\nocLCQuXk5GjZsmWSpHbt2umrr75SRERE3QVYlgKUAAAAgsiyLEmN7e805yYAzq1AmSjgFTaPx6OE\nhAS5XC45HA5lZmaqoKCgWpvevXurRYsWkqRevXqptLS02nwOeAAAAABwagIGtrKyMsXFxfmnnU6n\nysrK6mz/2muv6eabb/ZPW5alvn37KjU1VTNmzDgD5QIAAABA4xEaaGbVbRD18+mnn+r111/X0qVL\n/Y8tXbpUbdu21fbt29WvXz8lJyerT58+p14tAAAAADQiAQNbbGysvF6vf9rr9crpdNZo980332jU\nqFEqLCxUeHi4//G2bdtKkqKiojRw4EB5PJ5aA1tOTo7/97S0NKWlpZ3sdgAAAADAeSfgoCOVlZVK\nSkrSwoULFRMTo549e9YYdKSkpETXX3+93nrrLV155ZX+x/ft2yefz6ewsDBVVFQoPT1d48ePV3p6\nevUCGHQEAADbYtARADj7AmWigFfYQkNDlZubq4yMDPl8PmVlZcntdisvL0+SlJ2draeeekq7du3S\n6NGjJUkOh0Mej0dbt27VoEGDJFUFv+HDh9cIawAAAACAugW8wnZOCuAKGwAAtsUVNgA4+055WH8A\nAAAAQPAQ2AAAAADApghsAAAAAGBTBDYAAAAAsCkCGwAAAADYFIENAAAAAGyKwAYAAAAANkVgAwAA\nAACbIrABAAAAgE0R2AAAAADApghsAAAAAGBTBDYAAAAAsCkCGwAAAADYFIENAAAAAGyKwAYAAAAA\nNkVgAwAAAACbIrABAAAAgE0R2AAAAADApghsAAAAAGBTBDYAAAAAsCkCGwAAAADYFIENAAAAAGzq\nhIGtsLBQycnJSkxM1OTJk2vMf/vtt9W1a1d16dJFV199tb755pt6LwsAAAAAqJtljDF1zfT5fEpK\nStKCBQsUGxurHj16KD8/X26329/m888/V6dOndSiRQsVFhYqJydHy5Ytq9eykmRZlgKUAAAAgsiy\nLEmN7e805yYAzq1AmSjgFTaPx6OEhAS5XC45HA5lZmaqoKCgWpvevXurRYsWkqRevXqptLS03ssC\nAAAAAOoWMLCVlZUpLi7OP+10OlVWVlZn+9dee00333zzKS0LAAAAAKguNNDMqtsg6ufTTz/V66+/\nrqVLl570sgAAAACAmgIGttjYWHm9Xv+01+uV0+ms0e6bb77RqFGjVFhYqPDw8JNaVpJycnL8v6el\npSktLe1ktgEAAAAAzksBBx2prKxUUlKSFi5cqJiYGPXs2bPGwCElJSW6/vrr9dZbb+nKK688qWUl\nBh0BAMDOGHQEAM6+QJko4BW20NBQ5ebmKiMjQz6fT1lZWXK73crLy5MkZWdn66mnntKuXbs0evRo\nSZLD4ZDH46lzWQAAAABA/QS8wnZOCuAKGwAAtsUVNgA4+055WH8AAAAAQPAQ2AAAAADApghsAAAA\nAGBTBDYAAAAAsCkCGwAAAADYFIENAAAAAGyKwAYAAAAANkVgAwAAAACbIrABAAAAgE0R2AAAAADA\npghsAAAAAGBTBDYAAAAAsCkCGwAAAADYVGiwCwAam+bNI7R3765gl3HOhIWFa8+encEuAwAAoEGy\njDEmqAVYloJcAnBOWZYlqTHt87zHgYas8R2zJI5bAM61QJmIWyIBAAAAwKYIbAAAAABgUwQ2AAAA\nALApAhsAAAAA2BSBDQAAAABsisAGAAAAADZFYAMAAAAAmzphYCssLFRycrISExM1efLkGvPXrl2r\n3r1768IEmyw9AAATdElEQVQLL9Tzzz9fbZ7L5VKXLl2UkpKinj17nrmqAQAAAKARCA000+fzacyY\nMVqwYIFiY2PVo0cP9e/fX263298mMjJS06ZN09y5c2ssb1mWioqKFBERceYrBwAAAIDzXMArbB6P\nRwkJCXK5XHI4HMrMzFRBQUG1NlFRUUpNTZXD4ah1HXV9YzcAAAAAILCAga2srExxcXH+aafTqbKy\nsnqv3LIs9e3bV6mpqZoxY8apVwkAAAAAjVDAWyItyzqtlS9dulRt27bV9u3b1a9fPyUnJ6tPnz6n\ntU4AAAAAaCwCBrbY2Fh5vV7/tNfrldPprPfK27ZtK6nqtsmBAwfK4/HUGthycnL8v6elpSktLa3e\nzwEAAAAA56uAgS01NVXr1q1TcXGxYmJiNGfOHOXn59fa9vjPqu3bt08+n09hYWGqqKjQJ598ovHj\nx9e67LGBDQAAAABQJWBgCw0NVW5urjIyMuTz+ZSVlSW32628vDxJUnZ2trZu3aoePXpoz549CgkJ\n0UsvvaTVq1frxx9/1KBBgyRJlZWVGj58uNLT08/+FgEAAADAecIyQR7G0bIsRpJEo1L12dDGtM/z\nHgcassZ3zJI4bgE41wJlohN+cTYAAAAAIDgIbAAAAABgUwE/wwYAAACcDc2bR2jv3l3BLuOcCQsL\n1549O4NdBhogPsMGnGON7/MgvMeBhqzxHbMkjlvnRuPbt9ivUDc+wwYAAAAADRCBDQAAAABsisAG\nAAAAADZFYAMAAAAAmyKwAQAAAIBNEdgAAAAAwKYIbAAAAABgUwQ2AAAAALApAhsAAAAA2BSBDQAA\nAABsisAGAAAAADZFYAMAAAAAmyKwAQAAAIBNEdgAAAAAwKYIbAAAAABgUwQ2AAAAALApAhsAAAAA\n2BSBDQAAAABs6oSBrbCwUMnJyUpMTNTkyZNrzF+7dq169+6tCy+8UM8///xJLQsAAAAAqJtljDF1\nzfT5fEpKStKCBQsUGxurHj16KD8/X263299m+/bt2rRpk+bOnavw8HA9+uij9V5WkizLUoASgPOO\nZVmSGtM+z3scaMga3zFL4rh1bjS+fYv9CnULlIkCXmHzeDxKSEiQy+WSw+FQZmamCgoKqrWJiopS\namqqHA7HSS8LAAAAAKhbwMBWVlamuLg4/7TT6VRZWVm9Vnw6ywIAAAAAThDYqi5Vn5rTWRYAAAAA\nIIUGmhkbGyuv1+uf9nq9cjqd9VrxySybk5Pj/z0tLU1paWn1eg4AAAAAOJ8FDGypqalat26diouL\nFRMTozlz5ig/P7/Wtsd/SO5klj02sAEAAAAAqgQMbKGhocrNzVVGRoZ8Pp+ysrLkdruVl5cnScrO\nztbWrVvVo0cP7dmzRyEhIXrppZe0evVqXXLJJbUuCwAAAACon4DD+p+TAhjWH40MwxgDaEga3zFL\n4rh1bjS+fYv9CnU75WH9AQAAAADBQ2ADAAAAAJsisAEAAACATRHYAAAAAMCmCGwAAAAAYFMENgAA\nAACwKQIbAAAAANgUgQ0AAAAAbIrABgAAAAA2RWADAAAAAJsisAEAAACATYUGuwAAAAAAgTVvHqG9\ne3cFu4xzJiwsXHv27Ax2GbZgGWNMUAuwLAW5BOCcsixLUmPa53mPAw1Z4ztmSRy3zo3Gt2+d3n5F\nf53fAmUibokEAAAAAJsisAEAAACATRHYAAAAAMCmCGwAAAAAYFMENgAAAACwKQIbAAAAANgUgQ0A\nAAAAbIrABgAAAAA2RWADAAAAAJs6YWArLCxUcnKyEhMTNXny5Frb/OY3v1FiYqK6du2qlStX+h93\nuVzq0qWLUlJS1LNnzzNXNQAAAAA0AqGBZvp8Po0ZM0YLFixQbGysevToof79+8vtdvvbzJ8/X+vX\nr9e6dev0xRdfaPTo0Vq2bJkkybIsFRUVKSIi4uxuBQAAAACchwJeYfN4PEpISJDL5ZLD4VBmZqYK\nCgqqtZk3b57uuusuSVKvXr20e/dubdu2zT/fGHMWygYAAACA81/AwFZWVqa4uDj/tNPpVFlZWb3b\nWJalvn37KjU1VTNmzDiTdQMAAADAeS/gLZGWZdVrJXVdRfvHP/6hmJgYbd++Xf369VNycrL69Olz\n8lUCAAAAQCMUMLDFxsbK6/X6p71er5xOZ8A2paWlio2NlSTFxMRIkqKiojRw4EB5PJ5aA1tOTo7/\n97S0NKWlpZ30hgAAAADA+SZgYEtNTdW6detUXFysmJgYzZkzR/n5+dXa9O/fX7m5ucrMzNSyZcvU\nsmVLRUdHa9++ffL5fAoLC1NFRYU++eQTjR8/vtbnOTawAQAAAACqBAxsoaGhys3NVUZGhnw+n7Ky\nsuR2u5WXlydJys7O1s0336z58+crISFBzZo108yZMyVJW7du1aBBgyRJlZWVGj58uNLT08/y5gAA\nAADA+cMyQR7G0bIsRpJEo1L12dDGtM/zHgcassZ3zJI4bp0bjW/fOr39iv46vwXKRCf84mwAAAAA\nQHAQ2AAAAADApghsAAAAAGBTBDYAAAAAsCkCGwAAAADYFIENAAAAAGyKwAYAAAAANkVgAwAAAACb\nCg12AXbVvHmE9u7dFewyzpmwsHDt2bMz2GUAAAAAOIZlgvwV4oG+1TuY+DZ5nC3sWwAaksZ3zJJO\n57jFP3zrr/HtW6f395D+Or8FykQEtjrwpsDZwr4FoCFpfMcs6XSOW42vv+ir+iOwnZzGdf4QKBPx\nGTYAAAAAsCkCGwAAAADYFIENAAAAAGyKwAYAAAAANkVgAwAAAACbIrABAAAAgE0R2AAAAADApkKD\nXQAA4MzgC3sBADj/ENgA2BYB5ORU9VXj+ZLRvXutYJcAAMBZR2ADYFsEEJwt/DMAANBQENhw2hrb\niY/EyQ/Q0PHPAABAQ3HCQUcKCwuVnJysxMRETZ48udY2v/nNb5SYmKiuXbtq5cqVJ7UsGr7/nPg0\nnp/GFlABAAAQHAEDm8/n05gxY1RYWKjVq1crPz9fa9asqdZm/vz5Wr9+vdatW6dXXnlFo0ePrvey\nOBVFwS6gASkKdgENSFGwC2hgioJdQANSFOwCGpiiYBfQgBQFu4AGpCjYBTQwRcEuoAEpCnYBjULA\nwObxeJSQkCCXyyWHw6HMzEwVFBRUazNv3jzdddddkqRevXpp9+7d2rp1a72WxakoCnYBDUhRsAto\nQIqCXUADUxTsAhqQomAX0MAUBbuABqQo2AU0IEXBLqCBKQp2AQ1IUbALaBQCBraysjLFxcX5p51O\np8rKyurVZvPmzSdcFgAAAABQt4CBzbLq9yFlYxrPB7cBAAAA4FwJOEpkbGysvF6vf9rr9crpdAZs\nU1paKqfTqcOHD59wWUnq2rVrvYPhuWfXup48K2s9vdehcfWVdD72F311cuz4PpTs2V/sWyfHjvtW\n4+or6XzsL/rq5NjxfSjZs7/sum81LNdee22d8wIGttTUVK1bt07FxcWKiYnRnDlzlJ+fX61N//79\nlZubq8zMTC1btkwtW7ZUdHS0IiMjT7isJH399denuFkAAAAAcH4LGNhCQ0OVm5urjIwM+Xw+ZWVl\nye12Ky8vT5KUnZ2tm2++WfPnz1dCQoKaNWummTNnBlwWAAAAAFA/luEDaAAAAABgSyf84mwExz33\n3KPo6Ghdfvnl/sd27typfv36qWPHjkpPT9fu3buDWKG91NZfRz3//PMKCQnRzp07g1CZ/Xi9Xl13\n3XXq3LmzLrvsMk2dOlWStGrVKvXu3VtdunRR//79tXfv3iBXGnwHDhxQr1691K1bN3Xq1EmPPfaY\nJGns2LFyu93q2rWrBg0apJ9//jnIldqLz+dTSkqKbrnlFkn0V112796twYMHy+12q1OnTvriiy84\nztfh+++/V0pKiv+nRYsWeumll/THP/5RXbt2Vbdu3XTDDTdU++x8YzZx4kR17txZl19+uYYNG6aD\nBw/K4/GoZ8+eSklJUY8ePfTll18Gu0xbeOmll3T55Zfrsssu00svvSSJ861AauuvozjfOosMbOmz\nzz4zK1asMJdddpn/sbFjx5rJkycbY4yZNGmSGTduXLDKs53a+ssYY0pKSkxGRoZxuVxmx44dQarO\nXrZs2WJWrlxpjDFm7969pmPHjmb16tUmNTXVfPbZZ8YYY15//XXzxBNPBLNM26ioqDDGGHP48GHT\nq1cvs2TJEvPJJ58Yn89njDFm3LhxvBeP8/zzz5thw4aZW265xRhj6K863Hnnnea1114zxlTtX7t3\n7+Y4Xw8+n8+0adPGlJSUmD179vgfnzp1qsnKygpiZfawceNG065dO3PgwAFjjDFDhgwxb7zxhklL\nSzOFhYXGGGPmz59v0tLSglmmLXz77bfmsssuM/v37zeVlZWmb9++Zv369bwP61BXfxnD+dbZxhU2\nm+rTp4/Cw8OrPXbsl5Tfddddmjt3bjBKs6Xa+kuSHnnkEU2ZMiUIFdlXmzZt1K1bN0nSJZdcIrfb\nrbKyMq1bt059+vSRJPXt21fvvvtuMMu0jYsvvliSdOjQIfl8PkVERKhfv34KCak6fPbq1UulpaXB\nLNFWSktLNX/+fN17773+r3yhv2r6+eeftWTJEt1zzz2Sqj733aJFC47z9bBgwQJ16NBBcXFxCgsL\n8z9eXl6uVq1aBbEye2jevLkcDof27dunyspK7du3TzExMWrTpo3/6vbu3bsVGxsb5EqDb+3aterV\nq5cuvPBCNWnSRNdee63effdd3od1qK2/3nvvPUmcb51tBLYGZNu2bYqOjpYkRUdHa9u2bUGuyN4K\nCgrkdDrVpUuXYJdiW8XFxVq5cqV69eqlzp07q6CgQJL0zjvvcGvR/3fkyBF169ZN0dHRuu6669Sp\nU6dq819//XXdfPPNQarOfh5++GE999xz/oB2PPqrysaNGxUVFaWRI0fqiiuu0KhRo1RRUcFxvh5m\nz56tYcOG+acff/xxxcfHa9asWfrd734XxMrsISIiQo8++qji4+MVExOjli1bql+/fpo0aZL/8bFj\nx2rixInBLjXoLrvsMi1ZskQ7d+7Uvn37NH/+fJWWlvI+rENt/eX1ejnfOgcIbA2UZVmN6rspTta+\nffs0YcIEPfnkf74bxDC+TjXl5eUaPHiwXnrpJYWFhen111/X9OnTlZqaqvLycjVt2jTYJdpCSEiI\nvv76a5WWluqzzz5TUVGRf96zzz6rpk2bVjt5bMw++OADtW7dWikpKbW+3+iv/6isrNSKFSv0q1/9\nSitWrFCzZs00adKkam04ztd06NAhvf/++7r99tv9jz377LMqKSnR3XffrYcffjiI1dnDhg0b9OKL\nL6q4uFibN29WeXm53n77bWVlZWnq1KkqKSnRCy+84L+625glJydr3LhxSk9P10033aRu3bqpSZMm\n1drwPvyP2vrr4MGDmjhxIudbZxmBrQGJjo7W1q1bJUlbtmxR69atg1yRfW3YsEHFxcXq2rWr2rVr\np9LSUnXv3l0//vhjsEuzhcOHD+u2227THXfcoQEDBkiSkpKS9PHHH2v58uXKzMxUhw4dglylvbRo\n0UL/9V//peXLl0uS3njjDc2fP19vv/12kCuzj3/+85+aN2+e2rVrp6FDh2rRokW68847JdFfx3M6\nnXI6nerRo4ckafDgwVqxYoXatGnDcT6Ajz76SN27d1dUVFSNecOGDWMgDUnLly/XVVddpcjISIWG\nhmrQoEFaunSpPB6PBg4cKKlqf/N4PEGu1B7uueceLV++XIsXL1Z4eLg6duzI+VYAx/dX586dtXHj\nRs63zjICWwPSv39/zZo1S5I0a9Ys/4k2arr88su1bds2bdy4URs3bpTT6dSKFSs46KrqP19ZWVnq\n1KmTHnroIf/j27dvl1R1C+Azzzyj0aNHB6tE2/jpp5/8o4Pt379ff//735WSkqLCwkI999xzKigo\n0IUXXhjkKu1jwoQJ8nq92rhxo2bPnq3rr79eb775Jv1VizZt2iguLk4//PCDpKrPZXXu3Fm33HIL\nx/kA8vPzNXToUP/0unXr/L8XFBQoJSUlGGXZSnJyspYtW6b9+/fLGKOFCxeqU6dOSkxM1OLFiyVJ\nixYtUseOHYNcqT0cDRYlJSV67733NGzYMM63Aji+v+6++27Ot84BvofNpoYOHarFixfrp59+UnR0\ntJ566indeuutGjJkiEpKSuRyufTXv/5VLVu2DHaptnC0v3bs2KHWrVvrqaee0siRI/3z27dvr+XL\nlysiIiKIVdrDP/7xD11zzTXq0qWL/zaPCRMmaN26dfqf//kfSdJtt92mCRMmBLNMW/j222911113\n6ciRIzpy5IhGjBihsWPHKjExUYcOHfLvT71799b06dODXK29FBUV6c9//rPmzZtHf9Vh1apVuvfe\ne3Xo0CF16NBBM2fOlM/n4zhfh4qKCl166aXauHGjf7CRwYMH6/vvv1eTJk3UoUMHvfzyy5woSpoy\nZYpmzZqlkJAQXXHFFXr11Ve1atUq/frXv9bBgwd10UUXafr06QRcSddcc4127Nghh8OhF154Qddd\nd5127tzJ+7AOtfXXsTjfOjsIbAAAAABgU9wSCQAAAAA2RWADAAAAAJsisAEAAACATRHYAAAAAMCm\nCGwAAAAAYFMENgAAAACwKQIbAAAAANgUgQ0AAAAAbOr/ATP8R2d9/bqWAAAAAElFTkSuQmCC\n",
"text": "<matplotlib.figure.Figure at 0x10f067910>"
}
],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Section 3.2 Protein Descriptors\n"
},
{
"metadata": {},
"input": "from propy import PyPro\nfrom propy.GetProteinFromUniprot import GetProteinSequence\n\ndef Prot_descs(UniProtID,descs):\n proteinsequence=GetProteinSequence(UniProtID)\n DesObject=PyPro.GetProDes(proteinsequence)\n allD={}\n if 'CTD' in descs:\n allD = allD + DesObject.GetCTD() ##calculate 147 CTD descriptors\n if 'AAcomp' in descs:\n allD.update(DesObject.GetAAComp()) ##calculate 20 amino acid composition descriptors\n if 'PAAC' in descs:\n allD.update(DesObject.GetPAAC()) ##calculate 30 pseudo amino acid composition descriptors\n if 'DPC' in descs:\n allD.update(DesObject.GetDPComp()) #dipeptide composition descriptors\n if 'TPC' in descs:\n allD.update(DesObject.GetTPComp()) # tripeptide composition descriptors\n if 'QSO' in descs:\n allD.update(DesObject.GetQSO())\n #allD = dict(CTD.items() + AAcomp.items() + paac.items() + DPC.items() + TPC.items() + QSO.items())\n allD=np.asarray(allD.items())\n allD=np.transpose(allD)\n return allD\n\n# Example:\ndescsProt = Prot_descs('P00376',['AAcomp'])\nprint \"Descriptor names: \"\nprint descsProt[0]\nprint \"Descriptor values: \"\nprint descsProt[1].astype('float')",
"cell_type": "code",
"prompt_number": 302,
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "Descriptor names: \n['A' 'C' 'E' 'D' 'G' 'F' 'I' 'H' 'K' 'M' 'L' 'N' 'Q' 'P' 'S' 'R' 'T' 'W'\n 'V' 'Y']\nDescriptor values: \n[ 3.209 0.535 9.626 4.813 5.882 5.348 5.882 1.07 9.091 3.209\n 8.556 5.882 3.209 7.487 4.813 4.813 2.674 1.604 9.091 3.209]\n"
}
],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "#### Explanation of the descriptors calculated\n\n1. AAC: amino acid composition descriptors (20)\n2. DPC: dipeptide composition descriptors (400)\n3. TPC: tri-peptide composition descriptors (8000)\n4. MBauto: Normalized Moreau-Broto autocorrelation descriptors (depend on the given properties, the default is 240)\n5. Moranauto: Moran autocorrelation descriptors(depend on the given properties, the default is 240)\n6. Gearyauto: Geary autocorrelation descriptors(depend on the given properties, the default is 240)\n7. CTD: Composition, Transition, Distribution descriptors (CTD) (21+21+105=147)\n8. SOCN: sequence order coupling numbers (depend on the choice of maxlag, the default is 60)\n9. QSO: quasi-sequence order descriptors (depend on the choice of maxlag, the default is 100)\n10. PAAC: pseudo amino acid composition descriptors (depend on the choice of lamda, the default is 50)\n11. APAAC: amphiphilic pseudo amino acid composition descriptors(depend on the choice of lamda, the default is 50) "
},
{
"metadata": {},
"cell_type": "markdown",
"source": "###QSAM example\nDataset: ACE Inhibitors (courtesy of **Gerard Van Westen**)"
},
{
"metadata": {},
"input": "mols = Chem.SDMolSupplier('ACE_0003.sdf')\nmols = [x for x in mols if x is not None]\n",
"cell_type": "code",
"prompt_number": 303,
"outputs": [],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Get the amino acid descriptors (MS-WHIM):\n"
},
{
"metadata": {},
"input": "AAdescs=[]\nfor i,m in enumerate(mols):\n now=[]\n now.append(m.GetProp('MW1').split('\\n'))\n now.append(m.GetProp('MW2').split('\\n'))\n now.append(m.GetProp('MW3').split('\\n'))\n now=np.array(now)\n now=now.astype('float')\n now=now.flatten()\n AAdescs.append(now)\n \nAAdescs=np.array(AAdescs)",
"cell_type": "code",
"prompt_number": 304,
"outputs": [],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Get the response variable values:\n"
},
{
"metadata": {},
"input": "pIC50=[]\nfor i,m in enumerate(mols):\n pIC50.append(m.GetProp('pIC50'))\npIC50 = np.array(pIC50).astype('float')",
"cell_type": "code",
"prompt_number": 305,
"outputs": [],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "####Modeling of the ACE dataset\n\nDivide the original dataset into training and test set:"
},
{
"metadata": {},
"input": "X_train, X_test, y_train, y_test = cross_validation.train_test_split(AAdescs, pIC50, test_size=0.3, random_state=23)\n\nX_train = preprocessing.scale(X_train)\nX_test = preprocessing.scale(X_test)\n\n# Remove near zero variance descriptors\nto_keep=rmNearZeroVarDescs(AAdescs,30/1)\nX_train=X_train[:,to_keep]\nX_test=X_test[:,to_keep]\n\n## Grid search\ngammas = np.logspace(-10,4,8,base=2)\nCost = [0.001,0.001,0.01,1,10,100,1000]\nparam_grid = [\n {'C': Cost , 'gamma': gammas, 'kernel': ['rbf']},\n ]\n\ncv_stratified = StratifiedKFold(y_train, n_folds=10)\n\nclf_qsam = grid_search.GridSearchCV(estimator=SVR(), param_grid=param_grid, cv=cv_stratified,scoring='mean_squared_error') # or r\nmodel_qsam = clf_qsam.fit(X_train,y_train) ",
"cell_type": "code",
"prompt_number": 330,
"outputs": [],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"input": "ztest = model_qsam.predict(X_test)\nztest_train = model_qsam.predict(X_train)\n\nfigure,(plt1,plt2) = pylab.subplots(1,2)\nfigure.set_size_inches(15,5)\nplt1.scatter(y_test,ztest,c='green',label='test')\nminn = np.min(np.concatenate([y_test,ztest],axis=0))\nmaxx = np.max(np.concatenate([y_test,ztest],axis=0))\nplt1.set_ylim([minn,maxx])\nplt1.set_xlim([minn,maxx])\nplt1.set_title(\"Prediction test set\")\n\nplt2.scatter(y_train,ztest_train,c='green',label='test')\nplt2.set_title(\"Prediction training set\")\nminn = np.min(np.concatenate([y_train,ztest_train],axis=0))\nmaxx = np.max(np.concatenate([y_train,ztest_train],axis=0))\nplt2.set_ylim([minn,maxx])\nplt2.set_xlim([minn,maxx])",
"cell_type": "code",
"prompt_number": 331,
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 331,
"text": "(1.9400215403990133, 5.7999999999999998)"
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAA2YAAAFCCAYAAACad873AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nY7MAAAAASUVORK5CYII=\n",
"text": "<matplotlib.figure.Figure at 0x10fec9e10>"
}
],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Section 4. Model Validation\n"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "The definition of these validation metrics can be found here:\n\nTropsha, A.; Golbraikh, A. Predictive Quantitative Structure-Activity Relationships Modeling: \nDevelopment and Validation of QSAR Models. In: Handbook of Chemoinformatics Algorithms \n(Faulon, J.-L.; Bender, A., Eds.), Chapter 7, pp. 213-233, Chapman & Hall / CRC, London, UK, 2010.\n"
},
{
"metadata": {},
"input": "def slope(pred,true):\n return ( np.sum(true*pred) / np.sum(pred*pred) )\n\ndef Rsquared0(pred,true):\n true_mean = true.mean()\n yr0 = pred * slope(pred,true)\n first_term = (true - yr0)**2 #(true - yr0)\n second_term = (true-true_mean)**2 #(true-true_mean)\n return (1 - ( np.sum(first_term) / np.sum(second_term) ) )\n \ndef Rsquared(pred,true):\n true_mean = true.mean()\n pred_mean = pred.mean()\n first_term = np.sum( (true-true_mean) * (pred - pred_mean) )\n second_term = np.sqrt( np.sum( (true-true_mean)**2) * np.sum( (true-pred_mean)**2) )\n div = first_term / second_term\n return div\n\ndef Qsquared(pred,true):\n true_mean = true.mean()\n first_term = (np.abs(pred-true))**2\n second_term = (np.abs(true-true_mean))**2\n ret = 1 - (np.sum(first_term) / np.sum(second_term))\n return ret\n\ndef RMSE(pred,true):\n rmse = np.sqrt(mean_squared_error(true,pred))\n return rmse\n\n\nclass ValidationMetrics:\n def __init__(self, pred,true):\n self.RMSE = RMSE(pred,true)\n self.Qsquared = Qsquared(pred,true)\n self.Rsquared = Rsquared(pred,true)\n self.Rsquared0 = Rsquared0(pred,true)\n self.slope = slope(pred,true)",
"cell_type": "code",
"prompt_number": 322,
"outputs": [],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Calculation of model validation metrics\n"
},
{
"metadata": {},
"input": "metrics = ValidationMetrics(ztest_train,y_train)\nprint \"RMSE: \", metrics.RMSE\nprint \"Q squared: \", metrics.Qsquared\nprint \"R squared: \", metrics.Rsquared\nprint \"R squared 0: \", metrics.Rsquared0\nprint \"Slope: \", metrics.slope",
"cell_type": "code",
"prompt_number": 333,
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "RMSE: 0.414964575791\nQ squared: 0.85893790459\nR squared: 0.826934286524\nR squared 0: 0.859280795081\nSlope: 0.99376282785\n"
}
],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "\n##Section 5. RDkit with pandas\n"
},
{
"metadata": {},
"input": "# Load the molecules with pandas\ncpds = PandasTools.LoadSDF('mols_OK_with_properties.sdf')\n# The columns are:\nprint cpds.columns\n# Assign field names\nmolCAS = 'CAS_Number'\nsmiles = 'SMILES'\nmolID = 'Name'\nlogS = 'logS'\nmolImage = 'ROMol'",
"cell_type": "code",
"prompt_number": 2,
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "Index([u'CAS_Number', u'ID', u'LogS_Workshop', u'Name', u'SMILES', u'Source', u'logS', u'ROMol'], dtype=object)\n"
}
],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"input": "cpds[[smiles,molID,logS,molCAS]].head(1)\n# We can keep this information in a new data.frame\ncpds_short = cpds[[smiles,molID,logS,molCAS]].head(1)\ncpds_short",
"cell_type": "code",
"prompt_number": 197,
"outputs": [
{
"html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>SMILES</th>\n <th>Name</th>\n <th>logS</th>\n <th>CAS_Number</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td> CC(N)=O</td>\n <td> Example Molecule No 0</td>\n <td> 1.58</td>\n <td> 60-35-5</td>\n </tr>\n </tbody>\n</table>\n</div>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 197,
"text": " SMILES Name logS CAS_Number\n0 CC(N)=O Example Molecule No 0 1.58 60-35-5"
}
],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "####Some useful df attributes \n\nuseful feature | pandas syntax|\n----: | ----:\ncpds.tail() | cpds[1:4]\nMean (etc.) by columns | cpds.mean(0)\nor by rows | cpds.mean(1)\nequivalent to R's rownames() | cpds.index\nequivalent to R's colnames() | cpds.columns\nWe can access the data by column name | e.g. cpds.Name\nWe can directly sum etc. concrete columns | e.g. cpds.LogP + cpds.LogP"
},
{
"metadata": {
"run_control": {
"breakpoint": false
}
},
"input": "",
"cell_type": "code",
"outputs": [],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "#### Some of the options (http://pandas.pydata.org/pandas-docs/stable/basics.html)\n\nFunction |\tDescription\n---: | ---:\ncount |\tNumber of non-null observations\nsum |\tSum of values\nmean |\tMean of values\nmad |\tMean absolute deviation\nmedian |\tArithmetic median of values\nmin |\tMinimum\nmax |\tMaximum\nabs |\tAbsolute Value\nprod |\tProduct of values\nstd |\tUnbiased standard deviation\nvar |\tUnbiased variance\nskew |\tUnbiased skewness (3rd moment)\nkurt |\tUnbiased kurtosis (4th moment)\nquantile |\tSample quantile (value at %)\ncumsum |\tCumulative sum\ncumprod |\tCumulative product\ncummax |\tCumulative maximum\ncummin |\tCumulative minimum"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "#### Likewise, we can also perform comparisons, returning a boolean dataframe of dimensions equal to the original one:\n\n- Perform “rich comparisons” between a and b. Specifically, lt(a, b) is equivalent to a < b \n- le(a, b) is equivalent to a <= b, eq(a, b) is equivalent to a == b\n- ne(a, b) is equivalent to a != b, gt(a, b) is equivalent to a > b \n- ge(a, b) is equivalent to a >= b"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Data exploration"
},
{
"metadata": {},
"input": "a = cpds['logS'].astype('float')\na.hist()\nplt.title(\"LogS Distribution\")",
"cell_type": "code",
"prompt_number": 206,
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 206,
"text": "<matplotlib.text.Text at 0x10f628110>"
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAeUAAAF6CAYAAAAnAED0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text": "<matplotlib.figure.Figure at 0x10f107e10>"
}
],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "The data can be polished. For instance:\n"
},
{
"metadata": {
"run_control": {
"breakpoint": false
}
},
"input": "cpds = cpds.dropna() # drop NAs\ncpds = cpds.drop_duplicates(molCAS) # drop molecules duplicated by name\ncpds = cpds.drop_duplicates(smiles)",
"cell_type": "code",
"outputs": [],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Or we can add descriptors:\n"
},
{
"metadata": {},
"input": "cpds['logP'] = cpds['ROMol'].map(Descriptors.MolLogP)\ncpds['MW'] = cpds['ROMol'].map(Descriptors.MolWt)\nMolW = 'MW'\nlogP = 'logP'\ncpds[[smiles,molID,molCAS,logS,logP,MolW,'ROMol']].head(1)",
"cell_type": "code",
"prompt_number": 3,
"outputs": [
{
"html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>SMILES</th>\n <th>Name</th>\n <th>CAS_Number</th>\n <th>logS</th>\n <th>logP</th>\n <th>MW</th>\n <th>ROMol</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td> CC(N)=O</td>\n <td> Example Molecule No 0</td>\n <td> 60-35-5</td>\n <td> 1.58</td>\n <td>-0.5084</td>\n <td> 59.068</td>\n <td> &lt;img src=\"data:image/png;base64,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\" alt=\"Mol\"/&gt;</td>\n </tr>\n </tbody>\n</table>\n</div>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": " SMILES Name CAS_Number logS logP MW \\\n0 CC(N)=O Example Molecule No 0 60-35-5 1.58 -0.5084 59.068 \n\n ROMol \n0 <img src=\"data:image/png;base64,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\" alt=\"Mol\"/> "
}
],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Subsetting:"
},
{
"metadata": {},
"input": "print cpds.columns\nsel = cpds[['Name','logS',molID]]\nprint sel.columns",
"cell_type": "code",
"prompt_number": 107,
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "Index([u'CAS_Number', u'ID', u'LogS_Workshop', u'Name', u'SMILES', u'Source', u'logS', u'ROMol'], dtype=object)\nIndex([u'Name', u'logS', u'Name'], dtype=object)\n"
}
],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"input": "\n# By rows (index)\nprint sel.iloc[:2]\nprint sel[:2]\n# By column\nprint sel.icol(1)[:2]\n\n# To show in table, just remove 'print'\nsel.iloc[:2]",
"cell_type": "code",
"prompt_number": 125,
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": " Name logS Name\n0 Example Molecule No 0 1.58 Example Molecule No 0\n1 Example Molecule No 1 1.34 Example Molecule No 1\n Name logS Name\n0 Example Molecule No 0 1.58 Example Molecule No 0\n1 Example Molecule No 1 1.34 Example Molecule No 1\n0 1.58\n1 1.34\nName: logS, dtype: object\n"
},
{
"html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Name</th>\n <th>logS</th>\n <th>Name</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td> Example Molecule No 0</td>\n <td> 1.58</td>\n <td> Example Molecule No 0</td>\n </tr>\n <tr>\n <th>1</th>\n <td> Example Molecule No 1</td>\n <td> 1.34</td>\n <td> Example Molecule No 1</td>\n </tr>\n </tbody>\n</table>\n</div>",
"output_type": "pyout",
"prompt_number": 125,
"text": " Name logS Name\n0 Example Molecule No 0 1.58 Example Molecule No 0\n1 Example Molecule No 1 1.34 Example Molecule No 1"
}
],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "We can slice both rows and columns\n"
},
{
"metadata": {},
"input": "print \"first Column\"\nprint sel.iloc[:2].icol(0), \"\\n\"\nprint \"Second Column\"\nprint sel.iloc[:2].icol(1), \"\\n\"\nsel.iloc[:2].icol(2)",
"cell_type": "code",
"prompt_number": 138,
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "first Column\n0 Example Molecule No 0\n1 Example Molecule No 1\nName: Name, dtype: object \n\nSecond Column\n0 1.58\n1 1.34\nName: logS, dtype: object \n\n"
},
{
"output_type": "pyout",
"prompt_number": 138,
"text": "0 Example Molecule No 0\n1 Example Molecule No 1\nName: Name, dtype: object"
}
],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Adding and/or replacing data\n"
},
{
"metadata": {},
"input": "cpds['new']=cpds[molID]\nprint cpds['new'].head(2), \"\\n\"\ncpds['new']=cpds['SMILES']\nprint cpds['new'].head(2), \"\\n\"",
"cell_type": "code",
"prompt_number": 150,
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "0 Example Molecule No 0\n1 Example Molecule No 1\nName: new, dtype: object \n\n0 CC(N)=O\n1 CNN\nName: new, dtype: object \n\n"
}
],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Grouping the data:"
},
{
"metadata": {},
"input": "\n\n# Example 1. LogP > 5\ncpds.groupby(cpds[logP] >= 5).describe().unstack()\n\n# Example 2. Check which molecules contain a substructure (e.g. benzene)\nbenzene = Chem.MolFromSmiles('c1ccccc1')\ncpds.groupby(cpds['ROMol'] >= benzene).describe().unstack()\n",
"cell_type": "code",
"prompt_number": 169,
"outputs": [
{
"html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr>\n <th></th>\n <th colspan=\"8\" halign=\"left\">logP</th>\n <th colspan=\"8\" halign=\"left\">MW</th>\n </tr>\n <tr>\n <th></th>\n <th>count</th>\n <th>mean</th>\n <th>std</th>\n <th>min</th>\n <th>25%</th>\n <th>50%</th>\n <th>75%</th>\n <th>max</th>\n <th>count</th>\n <th>mean</th>\n <th>std</th>\n <th>min</th>\n <th>25%</th>\n <th>50%</th>\n <th>75%</th>\n <th>max</th>\n </tr>\n <tr>\n <th>ROMol</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>False</th>\n <td> 594</td>\n <td> 1.560804</td>\n <td> 1.653092</td>\n <td>-8.42420</td>\n <td> 0.72822</td>\n <td> 1.66010</td>\n <td> 2.41795</td>\n <td> 6.6944</td>\n <td> 594</td>\n <td> 165.131732</td>\n <td> 92.962655</td>\n <td> 46.073</td>\n <td> 102.14025</td>\n <td> 131.752</td>\n <td> 200.3220</td>\n <td> 665.733</td>\n </tr>\n <tr>\n <th>True </th>\n <td> 686</td>\n <td> 2.842855</td>\n <td> 1.688647</td>\n <td>-3.10802</td>\n <td> 1.68615</td>\n <td> 2.58192</td>\n <td> 3.64680</td>\n <td> 9.8876</td>\n <td> 686</td>\n <td> 226.742765</td>\n <td> 87.041802</td>\n <td> 78.114</td>\n <td> 152.16075</td>\n <td> 214.650</td>\n <td> 283.4045</td>\n <td> 650.976</td>\n </tr>\n </tbody>\n</table>\n</div>",
"output_type": "pyout",
"prompt_number": 169,
"text": " logP \\\n count mean std min 25% 50% 75% max \nROMol \nFalse 594 1.560804 1.653092 -8.42420 0.72822 1.66010 2.41795 6.6944 \nTrue 686 2.842855 1.688647 -3.10802 1.68615 2.58192 3.64680 9.8876 \n\n MW \\\n count mean std min 25% 50% 75% \nROMol \nFalse 594 165.131732 92.962655 46.073 102.14025 131.752 200.3220 \nTrue 686 226.742765 87.041802 78.114 152.16075 214.650 283.4045 \n\n \n max \nROMol \nFalse 665.733 \nTrue 650.976 "
}
],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Writing data to file\n`PandasTools.SaveSMILESFromFrame(cpds[smiles], 'smiles_test.smi', NamesCol=cpds[molCAS])`"
},
{
"metadata": {},
"input": "a = cpds.SMILES\nfrom cStringIO import StringIO\na.to_csv(\"smiles_pandas.csv\",sep=\",\",index=None) # more: http://pandas.pydata.org/pandas-docs/stable/io.html\n\nb = pd.read_csv(\"smiles_pandas.csv\",sep=\",\",header=None)\n# We can (e.g.) change row names\nb.index = cpds.CAS_Number.iloc[0:]\nb.head(2)",
"cell_type": "code",
"prompt_number": 207,
"outputs": [
{
"html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>0</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>60-35-5</th>\n <td> CC(N)=O</td>\n </tr>\n <tr>\n <th>60-34-4</th>\n <td> CNN</td>\n </tr>\n </tbody>\n</table>\n</div>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 207,
"text": " 0\n60-35-5 CC(N)=O\n60-34-4 CNN"
}
],
"language": "python",
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "\n## END OF THE TUTORIAL\n"
},
{
"metadata": {},
"input": "# convert the RDKit explicit vectors into numpy arrays\nnp_fps = []\nfor fp in fps:\n arr = numpy.zeros((1,))\n DataStructs.ConvertToNumpyArray(fp, arr)\n np_fps.append(arr)",
"cell_type": "code",
"outputs": [],
"language": "python",
"collapsed": false
}
],
"metadata": {}
}
],
"metadata": {
"name": "RDkit_Introduction"
},
"nbformat": 3
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment