def load():
pd.set_option('display.max_columns', 10)
pd.set_option('display.max_rows', 100)
# Download the Titanic dataset
titanic.download_dataset('titanic_dataset.csv')
# Load CSV file, indicate that the first column represents labels
data, labels = load_csv('titanic_dataset.csv',
target_column=0,
has_header=True,
categorical_labels=False,
n_classes=2)
# Make a df out of it for convenience
df = pd.DataFrame(data,
columns=[
"pclass", "name", "sex", "age", "sibsp", "parch",
"ticket", "fare"
])
df = df.drop(columns=['name', 'ticket'])
# bin ages
df['age'] = df['age'].astype('float64')
group_names = ['A', 'B', 'C', 'D', 'E']
bins = [-1, 17, 35, 50, 65, 1000]
df['age_group'] = pd.cut(df['age'], bins=bins, labels=group_names)
res = pd.get_dummies(df['age_group'], prefix='age_group')
df = pd.concat([df, res], axis=1)
df = df.drop(columns=['age', 'age_group'])
# normalize parch
# df['parch'] = df['parch'].astype('float64')
# df["parch"] = df["parch"] / df["parch"].max()
# normalize age
# df['age'] = df['age'].astype('float64')
# df["age"] = df["age"] / df["age"].max()
# normalize fare
df['fare'] = df['fare'].astype('float64')
df["fare"] = df["fare"] / df["fare"].max()
# normalize sibsp
df['sibsp'] = df['sibsp'].astype('float64')
df["sibsp"] = df["sibsp"] / df["sibsp"].max()
# normalize parch
df['parch'] = df['parch'].astype('float64')
df["parch"] = df["parch"] / df["parch"].max()
# convert sex
df = df.replace(["male", "female"], [0, 1])
print(df)
return df, labels
def prepare_csv():
# Download the Titanic dataset
from tflearn.datasets import titanic
titanic.download_dataset('titanic_dataset.csv')
# Load CSV file, indicate that the first column represents labels
from tflearn.data_utils import load_csv
data, labels = load_csv('titanic_dataset.csv',
target_column=0,
has_header=False,
categorical_labels=True,
n_classes=2)
# Preprocess data
data = preprocess_csv(data, to_ignore)
return data, labels
def load_titanic():
import tflearn
import numpy as np
"""
Download Titanic dataset
len = 1309
"""
from tflearn.datasets import titanic
titanic.download_dataset('titanic_dataset.csv')
# Load CSV file, indicate that the first column represents labels
from tflearn.data_utils import load_csv
data, titanic_labels = load_csv('titanic_dataset.csv',
target_column=0,
categorical_labels=True,
n_classes=2)
# Preprocessing function
def preprocess(passengers, columns_to_delete):
# Sort by descending id and delete columns
for column_to_delete in sorted(columns_to_delete, reverse=True):
[passenger.pop(column_to_delete) for passenger in passengers]
for i in range(len(passengers)):
# Converting 'sex' field to float (id is 1 after removing labels column)
passengers[i][1] = 1. if passengers[i][1] == 'female' else 0.
return np.array(passengers, dtype=np.float32)
# Ignore 'name' and 'ticket' columns (id 1 & 6 of data array)
to_ignore = [1, 6]
# Preprocess data
x_train = preprocess(data, to_ignore)
y_train = titanic_labels
x_test = x_train
y_test = y_train
input_dim = (6, )
num_classes = 2
return x_train, y_train, x_test, y_test, input_dim, num_classes
import tflearn
import numpy as np
from tflearn.datasets import titanic
titanic.download_dataset('titanic_dataset.csv')
from tflearn.data_utils import load_csv
data, labels = load_csv('titanic_dataset.csv',
target_column=0,
categorical_labels=True,
n_classes=2)
def preprocess(data, columns_to_ignore):
# Sort by descending id and delete columns
for id in sorted(columns_to_ignore, reverse=True):
[r.pop(id) for r in data]
for i in range(len(data)):
# Converting 'sex' field to float (id is 1 after removing labels column)
data[i][1] = 1. if data[i][1] == 'female' else 0.
return np.array(data, dtype=np.float32)
# Ignore 'name' and 'ticket' columns (id 1 & 6 of data array)
to_ignore = [1, 6]
# Preprocess data
data = preprocess(data, to_ignore)
net = tflearn.input_data(shape=[None, 6])
# -*- coding: utf-8 -*-
from __future__ import print_function
import numpy as np
import tflearn
# Download the Titanic dataset
from tflearn.datasets import titanic
titanic.download_dataset('titanic_dataset.csv')
# Load CSV file, indicate that the first column represents labels
from tflearn.data_utils import load_csv
data, labels = load_csv('titanic_dataset.csv', target_column=0,
categorical_labels=True, n_classes=2)
# Preprocessing function
def preprocess(data, columns_to_ignore):
# Sort by descending id and delete columns
for id in sorted(columns_to_ignore, reverse=True):
[r.pop(id) for r in data]
for i in range(len(data)):
# Converting 'sex' field to float (id is 1 after removing labels column)
data[i][1] = 1. if data[i][1] == 'female' else 0.
return np.array(data, dtype=np.float32)
# Ignore 'name' and 'ticket' columns (id 1 & 6 of data array)
to_ignore=[1, 6]
# Preprocess data
data = preprocess(data, to_ignore)
import tflearn
import numpy
from tflearn.datasets import titanic
titanic.download_dataset("titanic_dataset.csv")
from tflearn.data_utils import load_csv
data, labels = load_csv(
"titanic_dataset.csv",
target_column=0,
categorical_labels=True,
n_classes=2,
columns_to_ignore=[2, 7]) # two target columns/labels :survived or dead
for p in data:
if p[1] == "female":
p[1] = 1
else:
p[1] = 0
net = tflearn.input_data(shape=[None,
6]) # first layer of network has 6 layers
net = tflearn.fully_connected(net, 20)
net = tflearn.fully_connected(net, 20)
net = tflearn.fully_connected(net, 2, activation='softmax')
net = tflearn.regression(net)
# define model
model = tflearn.DNN(net)
from __future__ import print_function
import numpy as np
import tflearn
import pandas as pd
from tflearn.datasets import titanic
from tflearn.data_utils import load_csv
filename = 'var/titanic_dataset.csv'
titanic.download_dataset(filename)
dataset = pd.read_csv(filename)
labels = pd.get_dummies(dataset['survived']).values
dataset = dataset.drop(columns='name').drop(columns='ticket').drop(
columns='survived')
dataset['sex'] = dataset['sex'].apply(lambda sex: 1 if sex == 'female' else 0)
log_dir = ''
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir,
histogram_freq=1)
data = dataset.values
# Build neural network
net = tflearn.input_data(shape=[None, 6])
net = tflearn.fully_connected(net, 32)
net = tflearn.fully_connected(net, 32)
net = tflearn.fully_connected(net, 2, activation='softmax')
net = tflearn.regression(net)
model = tflearn.DNN(net)
# Start training (apply gradient descent algorithm)
# -*- coding: utf-8 -*-
"""
Created on Fri Jul 21 10:10:50 2017
@author: O222069
"""
import numpy as np
import pandas as pd
import tflearn
#download the titanic dataset
from tflearn.datasets import titanic
data = pd.read_csv(titanic.download_dataset('titanic_dataset.csv'))
#load csv file
from tflearn.data_utils import load_csv, samplewise_std_normalization
data, labels = load_csv('titanic_dataset.csv',
target_column=0,
categorical_labels=True,
n_classes=2)
#preprocessing function
def preprocess(data, columns_to_ignore):
for id in sorted(columns_to_ignore, reverse=True):
[r.pop(id) for r in data]
for i in range(len(data)):
data[i][1] = 1. if data[i][1] == 'female' else 0.
return np.array(data, dtype=np.float32)
import numpy as np
import tflearn
from os.path import join, dirname
mypath = '/Users/raghav/mypython/'
# Download the Titanic dataset
from tflearn.datasets import titanic
titanic.download_dataset(join(mypath, 'titanic_dataset.csv'))
# Load CSV file, indicate that the first column represents labels
from tflearn.data_utils import load_csv
data, labels = load_csv(join(mypath, 'titanic_dataset.csv'),
target_column=0,
categorical_labels=True,
n_classes=2)
# Preprocessing function
def preprocess(data, columns_to_ignore):
# Sort by descending id and delete columns
for id in sorted(columns_to_ignore, reverse=True):
[r.pop(id) for r in data]
for i in range(len(data)):
# Converting 'sex' field to float (id is 1 after removing labels column)
data[i][1] = 1. if data[i][1] == 'female' else 0.
return np.array(data, dtype=np.float32)
# Ignore 'name' and 'ticket' columns (id 1 & 6 of data array)
to_ignore = [1, 6]
# DATE: Sep 22, 2018
# SUMMARY: Data Parser to scan through the various raw data
# module for parsing CSV data sets
from __future__ import print_function
import csv
import pandas as pd
import tensorflow as tf
import numpy as np
import math
import tflearn
from tflearn.datasets import titanic
from tflearn.data_utils import load_csv
# Going over the Titanic Machine learning example
DataSetFileLocation = 'titanic_dataset.csv'
titanic.download_dataset(DataSetFileLocation)
data, labels = load_csv(DataSetFileLocation,
target_column=0,
categorical_labels=True,
n_classes=2)
def preprocess(data, columns_to_ignore):
# Sort by descending id and delete columns
for id in sorted(columns_to_ignore, reverse=True):
[r.pop(id) for r in data]
for i in range(len(data)):
# Converting 'sex' field to float (id is 1 after removing labels column)
data[i][1] = 1. if data[i][1] == 'female' else 0.
return np.array(data, dtype=np.float32)
import datetime
print(datetime.datetime.now())
import numpy as np
import tflearn
from tflearn.datasets import titanic
print(datetime.datetime.now())
titanic.download_dataset("titanic_dataset.csv")
from tflearn.data_utils import load_csv
data, labels = load_csv( "titanic_dataset.csv", target_column=0, categorical_labels=True, n_classes=2 )
def preprocess(data, columns_to_ignore ) :
for id in sorted(columns_to_ignore,reverse=True) :
[r.pop(id) for r in data]
for i in range(len(data)):
data[i][1] = 1. if data[i][1] == "female" else 0.
return np.array(data, dtype=np.float32)
to_ignore=[1,6]
data = preprocess(data,to_ignore)
print("preprocess done", datetime.datetime.now())
#
# python2 using tflearn
import numpy as np
from tensorflow.contrib import learn
import tflearn
# Download the Titanic dataset
from tflearn.datasets import titanic
titanic.download_dataset('./data/titanic_dataset.csv')
# Load CSV file, indicate that the first column represents labels
from tflearn.data_utils import load_csv
data_1, labels = load_csv('./data/titanic_dataset.csv',
target_column=0,
categorical_labels=True,
n_classes=2)
data = []
for it in data_1:
data.append(it[1])
print data
tokenizer = learn.preprocessing.VocabularyProcessor(10)
data = list(tokenizer.fit_transform(data))
data = tflearn.data_utils.pad_sequences(data, maxlen=10)
print data
print labels
trainX = data
