compute.py

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf
import numpy as np
import pandas as pd
from model import InputForm
from flask import Flask, render_template, request


def compute ( a,b,c,d,e,z,g,h,i):


      # Preparing the data
      data_file_name = 'breast-cancer-wisconsin.data.txt'

      first_line = "id,clump_thickness,unif_cell_size,unif_cell_shape,marg_adhesion,single_epith_cell_size,bare_nuclei,bland_chrom,norm_nucleoli,mitoses,class"
      with open(data_file_name, "r+") as f:
           content = f.read()
           f.seek(0, 0)
           f.write(first_line.rstrip('\r\n') + '\n' + content)
      df = pd.read_csv(data_file_name)
      df.replace('?', np.nan, inplace = True)
      df.dropna(inplace=True)
      df.drop(['id'], axis = 1, inplace = True)

      df['class'].replace('2',0, inplace = True)
      df['class'].replace('4',1, inplace = True)

      df.to_csv("combined_data.csv", index = False)

      # Data sets
      CANCER_TRAINING = "cancer_training.csv"
      CANCER_TEST = "cancer_test.csv"

      ######################################
      # classifier
      # Load datasets
      training_set_classifier = tf.contrib.learn.datasets.base.load_csv_with_header(filename=CANCER_TRAINING,
                                                       target_dtype=np.int, features_dtype=np.int)
      test_set_classifier =     tf.contrib.learn.datasets.base.load_csv_with_header(filename=CANCER_TEST,
                                                   target_dtype=np.int, features_dtype=np.int)

      feature_columns = [tf.contrib.layers.real_valued_column("", dimension=9)]

      classifier_classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,
                                              hidden_units=[10, 20, 10],
                                              n_classes=2,
                                              model_dir="/tmp/iris_model")
      #####################################
      # Accuracy
      # Load datasets.
      training_set_accuracy = tf.contrib.learn.datasets.base.load_csv_with_header(filename=CANCER_TRAINING,
                                                             target_dtype=np.int,
                                                             features_dtype=np.float32,
                                                             target_column=-1)
      test_set_accuracy = tf.contrib.learn.datasets.base.load_csv_with_header(filename=CANCER_TEST,
                                                         target_dtype=np.int,
                                                         features_dtype=np.float32,
                                                         target_column=-1)

      # Specify that all features have real-value data
      feature_columns = [tf.contrib.layers.real_valued_column("", dimension=2)]

      # Build 3 layer DNN with 10, 20, 10 units respectively.
      classifier_accuracy = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,
                                                  hidden_units=[10, 20, 10],
                                                  n_classes=2,
                                                  model_dir="/tmp/iris_model")


      ##########################################

      #classifier
      # Fitting model
      classifier_classifier = classifier_classifier.fit(training_set_classifier.data,
                              training_set_classifier.target,
                              steps=2000)
      k =a
      l = b
      m =c
      n= d
      o = e
      p = z
      q = g
      r = h
      s  = i

      def new_samples():
          return np.array([[k, l, m, n, o, p, q, r, s],
                   ], dtype=np.float32)

      s = list(classifier_classifier.predict(input_fn=new_samples))

      # Accuracy
      # Fit model.
      classifier_accuracy.fit(x=training_set_accuracy.data,
                     y=training_set_accuracy.target,
                     steps=2000)

      # Evaluate accuracy.
      accuracy_score = classifier_accuracy.evaluate(x=test_set_accuracy.data,
                                           y=test_set_accuracy.target)["accuracy"]

      if (s == [[1]]):
            return ("MALIGNANT",accuracy_score*100)
      else:
            return ("BENIGN",accuracy_score*100)


if __name__ == '__main__':

      print (compute(a,b,c,d,e,f,g,h,i))