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classification.py
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"""
BigDataMining - Part 2.BC
(B) - Features extract for classification
(C) - Classification
"""
import ast
import csv
import matplotlib as plt
import time
from sklearn import preprocessing
from sklearn.decomposition import TruncatedSVD
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import CountVectorizer, HashingVectorizer, TfidfTransformer
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.pipeline import Pipeline
from math import sqrt
import config
import numpy as np
import preprocess as prep
import pandas as pd
from gmplot import gmplot
########################################################################################################################
# Function to find min long/lat to use in grid #
########################################################################################################################
def min_borders():
df = pd.read_csv('tripsClean.csv')
lat = []
lon = []
clean_list = []
for i, row in df.iterrows():
trajectories = ast.literal_eval(row[2])
for j in range(0, len(trajectories)):
lon.append(float(trajectories[j][1]))
lat.append(float(trajectories[j][2]))
clean_list.append([float(trajectories[j][1]), float(trajectories[j][2])])
print "Minimum lat. is: ", min(lat)
print "Minimum long. is: ", min(lon)
return min(lat), min(lon)
########################################################################################################################
# Functions to extract features from data files #
########################################################################################################################
def features_extract():
filename = 'C_tripsClean.csv'
with open(filename, 'wb') as csvfile:
fieldnames = ['tripId', 'journeyPatternId', 'cells']
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writeheader()
df = pd.read_csv('tripsClean.csv')
minlat, minlon = min_borders()
for i, row in df.iterrows():
c = ""
trajectories = ast.literal_eval(row[2])
for j in range(0, len(trajectories)):
dx, dy = cell_pick(minlon, minlat, trajectories[j][1], trajectories[j][2], 0.3)
c += str("C" + str(int(dx)) + "," + str(int(dy)) + ";")
writer.writerow({'tripId': row[0], 'journeyPatternId': row[1], 'cells': c})
########################################################################################################################
def testset_features():
minlat, minlon = min_borders()
filename = 'C_test.csv'
with open(filename, 'wb') as csvfile:
fieldnames = ['tripId', 'cells']
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writeheader()
df = pd.read_csv(config.testsetPath, delimiter=';')
for i, row in df.iterrows():
c = ""
trajectories = ast.literal_eval(row[1])
for j in range(0, len(trajectories)):
dx, dy = cell_pick(minlon, minlat, trajectories[j][1], trajectories[j][2], 0.3)
c += str("C" + str(int(dx)) + "," + str(int(dy)) + ";")
writer.writerow({'tripId': row[0], 'cells': c})
########################################################################################################################
def cell_pick(minlon, minlat, lon, lat, csize):
distx = prep.haversine_dist(float(minlon), float(lat), float(minlon), float(minlat))
disty = prep.haversine_dist(float(lon), float(minlat), float(minlon), float(minlat))
return distx // csize, disty // csize
########################################################################################################################
# Functions for Classification and Prediction #
########################################################################################################################
def my_tokenizer(s):
return s.split(';')
########################################################################################################################
def classify():
df = pd.read_csv('C_tripsClean.csv')
# df = pd.read_csv('C_tripsClean_v5.csv')
# df = pd.read_csv('C_tripsClean_v6.csv')
label = preprocessing.LabelEncoder()
target_cat=[]
label.fit(df['journeyPatternId'])
x_train = df['cells']
y_train = label.transform(df['journeyPatternId'])
for jid in df['journeyPatternId']:
target_cat.append(jid)
countVect = CountVectorizer(tokenizer=my_tokenizer, ngram_range=(1,2))
hashVect = HashingVectorizer(tokenizer=my_tokenizer, ngram_range=(1,2))
# Count Vectorizer + KNN
cl_knn1 = Pipeline([('vect', countVect),
('clf', KNeighborsClassifier())])
# Hashing Vectorizer + KNN
cl_knn2 = Pipeline([('vect', hashVect),
('clf', KNeighborsClassifier())])
# Count Vectorizer + Logistic Regression
cl_lgr1 = Pipeline([('vect', countVect),
('clf', LogisticRegression())])
# Hashing Vectorizer + Logistic Regression
cl_lgr2 = Pipeline([('vect', hashVect),
('clf', LogisticRegression())])
# Count Vectorizer + Random Forest
cl_rf1 = Pipeline([('vect', countVect),
('clf', RandomForestClassifier())])
# Hashing Vectorizer + Random Forest
cl_rf2 = Pipeline([('vect', hashVect),
('clf', RandomForestClassifier())])
# Hashing Vectorizer + MPLPC classifier
cl_mlp = Pipeline([('vect', hashVect),
('clf', MLPClassifier(hidden_layer_sizes=(100, 100, 100), max_iter=500, alpha=0.0001,
solver='sgd', verbose=10, random_state=21, tol=0.000000001))
])
# /////////////// K-Nearest Neighbor optimizing ////////////// #
# myList = list(range(1, 20))
# cv_scores = []
# neighbors = filter(lambda x: x % 2 != 0, myList)
# for k in neighbors:
# start = time.time()
# cl_knn2 = Pipeline([('vect', hashVect),
# ('clf', KNeighborsClassifier(n_neighbors=k))])
#
# scores = cross_val_score(cl_knn2, x_train, y_train, cv=10, scoring='accuracy')
# cv_scores.append(scores.mean())
# end = time.time()
# print("KNN + HashVect with %d neighbors: Accuracy: %0.2f (+/- %0.2f) | dt = %f s" % ( k,
# scores.mean(), scores.std() * 2, float(end - start)))
# MSE = [1 - x for x in cv_scores]
#
# # determining best k
# optimal_k = neighbors[MSE.index(min(MSE))]
# print "The optimal number of neighbors is %d" % optimal_k
#
# # plot misclassification error vs k
# plt.plot(neighbors, MSE)
# plt.xlabel('Number of Neighbors K')
# plt.ylabel('Misclassification Error')
# plt.show()
# /////////////// K-Nearest Neighbor optimizing ////////////// #
# start = time.time()
# scores = cross_val_score(cl_knn1, x_train, y_train, cv=10)
# accrf = scores.mean()
# end = time.time()
# print("KNN + CountVect: Accuracy: %0.2f (+/- %0.2f) | dt = %f s" % (scores.mean(), scores.std() * 2, float(end - start)))
#
# start = time.time()
# scores = cross_val_score(cl_knn2, x_train, y_train, cv=10)
# accrf = scores.mean()
# end = time.time()
# print("KNN + HashVect: Accuracy: %0.2f (+/- %0.2f) | dt = %f s" % (scores.mean(), scores.std() * 2, float(end - start)))
#
# start = time.time()
# scores = cross_val_score(cl_lgr1, x_train, y_train, cv=10)
# accrf = scores.mean()
# end = time.time()
# print("LGR + CountVect: Accuracy: %0.2f (+/- %0.2f) | dt = %f s" % (scores.mean(), scores.std() * 2, float(end - start)))
#
# start = time.time()
# scores = cross_val_score(cl_lgr2, x_train, y_train, cv=10)
# accrf = scores.mean()
# end = time.time()
# print("LGR + HashVect: Accuracy: %0.2f (+/- %0.2f) | dt = %f s" % (scores.mean(), scores.std() * 2, float(end - start)))
# start = time.time()
# scores = cross_val_score(cl_rf1, x_train, y_train, cv=10)
# accrf = scores.mean()
# end = time.time()
# print("RF + CountVect: Accuracy: %0.2f (+/- %0.2f) | dt = %f s" % (scores.mean(), scores.std() * 2, float(end - start)))
#
# start = time.time()
# scores = cross_val_score(cl_rf2, x_train, y_train, cv=10)
# accrf = scores.mean()
# end = time.time()
# print("RF + HashVect: Accuracy: %0.2f (+/- %0.2f) | dt = %f s" % (scores.mean(), scores.std() * 2, float(end - start)))
# start = time.time()
# scores = cross_val_score(cl_mlp, x_train, y_train, cv=10)
# accrf = scores.mean()
# end = time.time()
# print("cl_mlp: Accuracy: %0.2f (+/- %0.2f) | dt = %f s" % (scores.mean(), scores.std() * 2, float(end - start)))
start = time.time()
predict(cl_rf1, x_train, target_cat)
end = time.time()
print float(end-start)
# start = time.time()
# predict(cl_knn2, x_train, target_cat)
# end = time.time()
########################################################################################################################
def predict(clf, x_train, target_cat):
testdata = pd.read_csv('C_test.csv')
# target_cat = np.array(target_cat)
test_tripid = []
for id in testdata['tripId']:
test_tripid.append(id)
clf.fit(x_train, target_cat)
test = testdata['cells']
predictions = clf.predict(test)
# with open('testSet_JourneyPatternIDs'+str(i)+'.csv', 'w') as csvfile:
with open('testSet_JourneyPatternIDs.csv', 'w') as csvfile:
fieldnames=['Test_Trip_ID', 'Predicted_JourneyPatternID']
writer = csv.DictWriter(csvfile, fieldnames=fieldnames, delimiter='\t')
writer.writeheader()
i=0
for row in predictions:
writer.writerow({'Test_Trip_ID': test_tripid[i], 'Predicted_JourneyPatternID': row})
i+=1
########################################################################################################################
# testset_features()
# features_extract()
classify()