Gene_Expression_Predict_Kaggle/RF.py at master · alitakin/Gene_Expression_Predict_Kaggle · GitHub

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import csv
import numpy as np
import matplotlib.pyplot as plt

import os
from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_auc_score


if __name__== '__main__':

    data_path = "C:\\Users\\Z RY\\Documents\\TUT\\Competetion\\Gene_Expression_Predict_Kaggle" # This folder holds the csv files

    # load csv files. We use np.loadtxt. Delimiter is ","
    # and the text-only header row will be skipped.

    print("Loading data...")
    x_train = np.loadtxt(data_path + os.sep + "x_train.csv",
                         delimiter = ",", skiprows = 1)
    x_test  = np.loadtxt(data_path + os.sep + "x_test.csv",
                         delimiter = ",", skiprows = 1)
    y_train = np.loadtxt(data_path + os.sep + "y_train.csv",
                         delimiter = ",", skiprows = 1)

    print ("All files loaded. Preprocessing...")

    # remove the first column(Id)
    x_train = x_train[:,1:]
    x_test  = x_test[:,1:]
    y_train = y_train[:,1:]

    # Every 100 rows correspond to one gene.
    # Extract all 100-row-blocks into a list using np.split.
    num_genes_train = x_train.shape[0] / 100
    num_genes_test  = x_test.shape[0] / 100

    print("Train / test data has %d / %d genes." % \
          (num_genes_train, num_genes_test))
    x_train = np.split(x_train, num_genes_train)
    x_test  = np.split(x_test, num_genes_test)

    # Reshape by raveling each 100x5 array into a 500-length vector
    x_train = [g.ravel() for g in x_train]
    x_test  = [g.ravel() for g in x_test]

    # convert data from list to array
    x_train = np.array(x_train)
    y_train = np.array(y_train)
    x_test  = np.array(x_test)
    y_train = np.ravel(y_train)

    # Now x_train should be 15485 x 500 and x_test 3871 x 500.
    # y_train is 15485-long vector.

    print("x_train shape is %s" % str(x_train.shape))
    print("y_train shape is %s" % str(y_train.shape))
    print("x_test shape is %s" % str(x_test.shape))

    print('Data preprocessing done...')

    print("Next steps FOR YOU:")
    print("-" * 30)
    print("1. Define a classifier using sklearn")
    print("2. Assess its accuracy using cross-validation (optional)")
    print("3. Fine tune the parameters and return to 2 until happy (optional)")
    print("4. Create submission file. Should be similar to y_train.csv.")
    print("5. Submit at kaggle.com and sit back.")

#    X_train, X_test, y_train, y_test = train_test_split(x_train, y_train, test_size=0.2)
#
    model = RandomForestClassifier()

    model.fit(x_train, y_train)
    y_pred = model.predict(x_test)
    y_pred_proba = model.predict_proba(x_test)


#    data = []
#    for i in range(np.shape(y_pred_proba)[0]):
#        data.append(np.round(np.max(y_pred_proba[i]),2))
#
##    b=dict(enumerate(data))
#
#    with open("test_csv_path.csv", "w", newline='') as csv_file:
#        writer = csv.writer(csv_file, delimiter=',')
#
#        for line in data:
#            writer.writerow(line)


##    acuracy = roc_auc_score(y_test, y_pred)
##    print(acuracy)
#
#    with open(csvfile, "wb") as csv_file:
#        writer = csv.writer(csv_file, delimiter = ',')
#        for val in data:
#            writer.writerow([val])
#    file = open('test_csv_path', 'r+')
##    header = next(file)
#    print('GeneId\tPrediction')
#    for i, f in enumerate(file):
#        print("%s\t%s" %(f.strip(),))
#    file.close()
#