breast_cancer_DL_classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Breast Cancer Wisconsin (Diagnostic) Data Set</b> \n",
    "(reference to UCI Machine Learning Repository <a href=\"http://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29\">description</a>)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# coding: utf-8\n",
    "import multiprocessing \n",
    "multiprocessing.set_start_method('spawn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using multiprocessing code, and running in Python 3, a work around is necessary for process forking by adding mp.set_start_method('spawn') to scripts. One also needs to make sure there are no CUDA calls prior to starting your subprocesses; torch.cuda.is_available() and torch.manual_seed both count as a CUDA calls in PyTorch 0.2.0. Such calls would initialize the CUDA layer causing subprocesses to hang if they try to re-initialize."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tb 2018-09-13 \n",
      "\n",
      "CPython 3.6.4\n",
      "IPython 6.2.1\n",
      "\n",
      "sys 3.6.4 |Anaconda custom (64-bit)| (default, Jan 16 2018, 12:04:33) \n",
      "[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]\n",
      "numpy 1.14.2\n",
      "pandas 0.22.0\n",
      "sklearn 0.19.2\n",
      "torch 0.4.0a0+9692519\n",
      "IPython 6.2.1\n",
      "\n",
      "compiler   : GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)\n",
      "system     : Darwin\n",
      "release    : 17.5.0\n",
      "machine    : x86_64\n",
      "processor  : i386\n",
      "CPU cores  : 24\n",
      "interpreter: 64bit\n",
      "\n",
      "GPU Name: TITAN Xp\n",
      "GPU Memory: 12.0GB\n",
      "CUDA Version: (9, 1, 0)\n",
      "GPU Free/Total Memory: 85%\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pylab import rcParams\n",
    "rcParams['figure.figsize'] = 14, 8\n",
    "\n",
    "from IPython.display import display\n",
    "import pickle\n",
    "\n",
    "import numpy as np\n",
    "np.set_printoptions(precision=4, threshold=10000, linewidth=100, edgeitems=999, suppress=True)\n",
    "\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_columns', 30)\n",
    "pd.set_option('display.width', 200)\n",
    "pd.set_option('display.max_rows', 500)\n",
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.width', 1000)\n",
    "pd.set_option('precision', 6)\n",
    "\n",
    "from collections import Counter\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "\n",
    "from tqdm import tqdm_notebook, tqdm\n",
    "\n",
    "from imblearn.over_sampling import RandomOverSampler, SMOTE\n",
    "from sklearn import metrics\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import ShuffleSplit, KFold\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler, Normalizer\n",
    "from sklearn.model_selection import learning_curve\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import cross_validate\n",
    "from sklearn.model_selection import ShuffleSplit, KFold\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score, KFold, RandomizedSearchCV\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import validation_curve\n",
    "\n",
    "import torch\n",
    "from torch import nn\n",
    "\n",
    "from lib.utils import *\n",
    "\n",
    "from lib.classifier import Classifier, torch_weight_init, Stopping, OutlierSampler, exp_decay, to_var, PytorchLogger\n",
    "\n",
    "from lib.model import MLP\n",
    "\n",
    "%load_ext watermark\n",
    "%watermark -a \"tb\" -d -v -m -p sys,numpy,pandas,sklearn,torch,IPython\n",
    "gpu_stat()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyperparameters\n",
    "HP = HYPERPARAMETERS({\n",
    "    \"GPU\"                 : True ,\n",
    "    \"SEED\"                : 0\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if not HP.SEED:\n",
    "#     torch.cuda.is_available = lambda : False\n",
    "\n",
    "# Seed random generators for repro \n",
    "np.random.seed(HP.SEED)  \n",
    "# torch.manual_seed(HP.SEED);\n",
    "# if torch.cuda.is_available():\n",
    "#     torch.cuda.manual_seed(HP.SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Dataset statistics:\n",
      "===================\n",
      "number of features:       30\n",
      "number of classes:        2\n",
      "data type:                float32\n",
      "number of train samples:  381 (pos=236, neg=145, size=0MB)\n",
      "number of test samples:   188 (pos=121, neg=67, size=0MB)\n",
      "labels                    ['malignant' 'benign']\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "\n",
    "dataset = load_breast_cancer()\n",
    "feature_names = dataset.feature_names\n",
    "target_names = dataset.target_names\n",
    "data = pd.DataFrame(np.c_[dataset['target'], dataset['data']], columns= np.append(['diagnosis'], feature_names ))\n",
    "\n",
    "X = np.float32(data.drop('diagnosis', axis=1).values)\n",
    "y = np.int64(data['diagnosis'].values)   # conversion to int64 for pytorch\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, shuffle=True, random_state=HP.SEED)\n",
    "\n",
    "dataset_statistics(X_train, y_train, X_test, y_test, target_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix\n",
      " [[ 64   3]\n",
      " [  1 120]]\n",
      "Accuracy  0.9787234042553191\n",
      "Recall    0.9917355371900827\n",
      "Precision 0.975609756097561\n",
      "F1        0.9836065573770492\n",
      "ROC AUC   0.9734797088935488\n"
     ]
    }
   ],
   "source": [
    "params = {\n",
    "    'module'                       : MLP,\n",
    "    'module_H'                     : 15,\n",
    "    'module_D_in'                  : X_train.shape[1] ,\n",
    "    'module_D_out'                 : 2 ,\n",
    "    'module_dropout'               : 0.5,\n",
    "\n",
    "    'module_initialize'            : torch_weight_init ,\n",
    "\n",
    "    'max_epochs'                   : 1000,\n",
    "    'batch_size'                   : 80 ,\n",
    "    'batch_shuffle'                : True,\n",
    "    'validation_ratio'             : 0.33,\n",
    "    'validation_shuffle'           : True,\n",
    "\n",
    "    'optimizer'                    : torch.optim.SGD ,\n",
    "    'optimizer_lr'                 : 0.1 ,\n",
    "    'optimizer_momentum'           : 0.9 ,\n",
    "    'optimizer_weight_decay'       : 5e-4 ,\n",
    "    'optimizer_nesterov'           : True ,\n",
    "\n",
    "    'criterion'                    : nn.CrossEntropyLoss ,\n",
    "    'criterion_size_average'       : True ,\n",
    "\n",
    "    'scheduler'                    : torch.optim.lr_scheduler.LambdaLR ,\n",
    "    'scheduler_lr_lambda'          : exp_decay,\n",
    "\n",
    "    'stopping'                     : Stopping ,\n",
    "    'stopping_patience'            : 100 ,\n",
    "\n",
    "    'scoring'                      : metrics.f1_score ,\n",
    "\n",
    "#     'progress_bar'                 : tqdm,\n",
    "\n",
    "    'random_state'                 : 10\n",
    "}\n",
    "\n",
    "pipeline = Pipeline(steps=[\n",
    "                ('scaler',     StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
    "                ('outlier',    OutlierSampler(8.5)),\n",
    "                ('sampler',    SMOTE(random_state=HP.SEED)),\n",
    "                ('classifier', Classifier(**params))\n",
    "                ])\n",
    "\n",
    "y_pred = pipeline.fit(X_train, y_train).predict(X_test)\n",
    "y_prob = pipeline.predict_proba(X_test)\n",
    "\n",
    "print('Confusion Matrix\\n', metrics.confusion_matrix(y_test, y_pred))  # Confusion Matrix\n",
    "print('Accuracy ', metrics.accuracy_score(y_test, y_pred))            # Accuracy (TP + TN) / float(TP + TN + FP + FN)\n",
    "print('Recall   ', metrics.recall_score(y_test, y_pred))              # Recall (Sensitivity)   TP / float(FN + TP)\n",
    "print('Precision', metrics.precision_score(y_test, y_pred))     \n",
    "print('F1       ', metrics.f1_score(y_test, y_pred))     \n",
    "print('ROC AUC  ', metrics.roc_auc_score(y_test, y_pred))   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy score: 0.9576023391812866 0.031688406786295524\n"
     ]
    }
   ],
   "source": [
    "#  Cross-validate score on test set\n",
    "cv = KFold(n_splits=10, shuffle=True, random_state=HP.SEED).split(X_test)\n",
    "scores = cross_val_score(pipeline, X_test, y_test, cv=cv, scoring='accuracy', verbose=0, n_jobs=-1)\n",
    "\n",
    "print(\"Test accuracy score:\", np.mean(scores), np.std(scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e24c978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the curves for loss and scores to check for overfitting\n",
    "clf = pipeline.named_steps['classifier']\n",
    "plot_loss_curve(clf.loss_train_, clf.score_train_,  clf.loss_valid_, clf.score_valid_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ad5878128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# https://www.ritchieng.com/machinelearning-learning-curve/ to interprete this curve\n",
    "cv = KFold(n_splits=10, shuffle=True, random_state=HP.SEED).split(X_train)\n",
    "_ = plot_learning_curve(pipeline, 'Learning Curves', X_train, y_train, cv=cv, shuffle=True, \n",
    "                    n_jobs=4, random_state=HP.SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ad4868518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Finetuning of hyperparameters using sklearn validation_curve \n",
    "# - uncomment the parameter to validate below and define a value range to evaluate\n",
    "# - result is a plot accuracy over parameter that visualizes the dependency\n",
    "\n",
    "# param_name ='classifier__optimizer_lr'\n",
    "# param_range = [  0.01, 0.1, 0.2, 0.3, 0.5, 0.8, 1.0, 1.1, 2.0]\n",
    "\n",
    "param_name ='classifier__module_H'\n",
    "param_range = [  15, 25, 30]\n",
    "\n",
    "# param_name ='classifier__stopping_patience'\n",
    "# param_range = [ 120, 150, 240]\n",
    "\n",
    "# param_name ='classifier__batch_size'\n",
    "# param_range = [ 70, 80, 90]\n",
    "\n",
    "cv = KFold(n_splits=3, shuffle=True, random_state=HP.SEED).split(X)\n",
    "train_scores, test_scores = validation_curve(\n",
    "                 estimator=pipeline,\n",
    "                 X=X,\n",
    "                 y=y,\n",
    "                 param_name=param_name,\n",
    "                 param_range=param_range,\n",
    "                 scoring = 'f1',\n",
    "                 cv=cv, n_jobs=4)\n",
    "\n",
    "train_mean = np.mean(train_scores, axis=1)\n",
    "train_std = np.std(train_scores, axis=1)\n",
    "test_mean = np.mean(test_scores, axis=1)\n",
    "test_std = np.std(test_scores, axis=1)\n",
    "plt.plot(param_range, train_mean,\n",
    "          color='blue', marker='o',\n",
    "          markersize=5,\n",
    "          label='training accuracy')\n",
    "plt.fill_between(param_range, train_mean + train_std,\n",
    "                  train_mean - train_std, alpha=0.15,\n",
    "                  color='blue')\n",
    "plt.plot(param_range, test_mean,\n",
    "          color='green', linestyle='--',\n",
    "          marker='s', markersize=5,\n",
    "          label='validation accuracy')\n",
    "plt.fill_between(param_range,\n",
    "                  test_mean + test_std,\n",
    "                  test_mean - test_std,\n",
    "                  alpha=0.15, color='green')\n",
    "plt.grid()\n",
    "# plt.xscale('log')\n",
    "plt.legend(loc='lower right')\n",
    "plt.xlabel('Parameter')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.ylim([0.8, 1.0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e307978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc_curve(y_test, y_pred, y_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ad5ad8860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_classifier_summary(y_test, y_pred, target_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ad57a8470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scatter_plots(X_test, y_pred, y_prob, y_test, target_names, figsize=(18,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: weighted average f1-score \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "  malignant       0.99      0.96      0.97       212\n",
      "     benign       0.98      0.99      0.98       357\n",
      "\n",
      "avg / total       0.98      0.98      0.98       569\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Data points=569'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Features=30'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Class dist.=0.627417'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'F1 valid=0.984701'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'ACC=0.980668'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'ROC_AUC=0.976930'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'LOG_LOSS=0.667720'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Misclassified=11'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Data points=[40, 73, 81, 91, 135, 213, 263, 297, 413, 514, 541]'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "classifier_summary_report(X, y, pipeline.predict(X), target_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary for model: MLP\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Shape                     Param #   \n",
      "=================================================================\n",
      "fc1.0 (Linear)               ((15, 30),)               450       \n",
      "_________________________________________________________________\n",
      "fc1.1 (BatchNorm1d)          ((15,), (15,))            30        \n",
      "_________________________________________________________________\n",
      "fc1.2 (ReLU)                 ()                        0         \n",
      "_________________________________________________________________\n",
      "fc2.0 (Linear)               ((15, 30),)               450       \n",
      "_________________________________________________________________\n",
      "fc2.1 (BatchNorm1d)          ((15,), (15,))            30        \n",
      "_________________________________________________________________\n",
      "fc2.2 (ReLU)                 ()                        0         \n",
      "_________________________________________________________________\n",
      "classifier.0 (Dropout)       ()                        0         \n",
      "_________________________________________________________________\n",
      "classifier.1 (Linear)        ((2, 15), (2,))           32        \n",
      "=================================================================\n",
      "Total params:         992\n",
      "Trainable params:     992\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "print_model_summary(pipeline.named_steps['classifier'].module_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ad5e03a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ad5e40160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ad5d4b438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ad5e11f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ad5e40fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1adc23ecc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1adc27b940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1adc3b4908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1adc3cb390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ad5dcc7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1adc3f46d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1adc52e940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1adc5ac400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1adc3adef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ad485c630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_layer_stats(pipeline.named_steps['classifier'].module_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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    "plot_model_graph(pipeline.named_steps['classifier'].module_(to_var(torch.from_numpy(X_test).float())), \n",
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