7 examples of 'import train test split' in Python

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9def train_test_split(fileName,type=1):
10 header = ['user_id', 'item_id', 'rating', 'timestamp']
11 if(type==1):
12 df = pd.read_csv(fileName, sep='\t', names=header)
13 else:
14 df = pd.read_csv(fileName, sep='::', names=header,engine = 'python')
15 n_users = df.user_id.unique().shape[0]
16 users = df.user_id.max()
17 n_items = df.item_id.unique().shape[0]
18 items = df.item_id.max()
19
20 print 'Number of users = ' + str(n_users) + ' | Number of movies = ' + str(n_items)
21 print 'The biggest ID of users = ' + str(users) + ' | The biggest ID of movies = ' + str(items)
22 #
23 train_data, test_data = cv.train_test_split(df, test_size=0.1)
24 train_data = pd.DataFrame(train_data)
25 test_data = pd.DataFrame(test_data)
26 #Create two user-item matrices, one for training and another for testing
27 train_data_matrix = np.zeros((users, items))
28 for line in train_data.itertuples():
29 train_data_matrix[line[1]-1, line[2]-1] = line[3]
30
31 test_data_matrix = np.zeros((users, items))
32 for line in test_data.itertuples():
33 test_data_matrix[line[1]-1, line[2]-1] = line[3]
34 return train_data_matrix,test_data_matrix
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23def train_valid_test_split(SOURCE_DATA_DIR, TARGET_DATA_DIR, train_size=0.8, valid_size=0.1,
24 COMBINE_FOLDERS=None, SELECT_FOLDERS=None):
25 """
26 Usage:
27 SOURCE_DATA_DIR = "data/ClothingAttributeDataset/images/"
28 TARGET_DATA_DIR = "data/"
29
30 train_valid_test_split(SOURCE_DATA_DIR, TARGET_DATA_DIR)
31 """
32 if COMBINE_FOLDERS is None:
33 COMBINE_FOLDERS = dict()
34 for folder_name in ["train", "test", "valid"]:
35 rmtree(os.path.join(TARGET_DATA_DIR, folder_name), ignore_errors=True)
36 os.makedirs(os.path.join(TARGET_DATA_DIR, folder_name))
37
38 # Split records by 80-20 between Train and Validation Set
39 filenames = np.random.permutation(glob(os.path.join(SOURCE_DATA_DIR, "*.jpg")))
40
41 train_idx = int(len(filenames) * train_size)
42 test_idx = int(len(filenames) * (train_size+valid_size))
43 for idx, filename in enumerate(filenames):
44 target_name = filename.split("/")[-1]
45 if idx < train_idx:
46 target_filepath = os.path.join(TARGET_DATA_DIR, "train", target_name)
47 elif idx < test_idx:
48 target_filepath = os.path.join(TARGET_DATA_DIR, "valid", target_name)
49 else:
50 target_filepath = os.path.join(TARGET_DATA_DIR, "test", target_name)
51 copyfile(filenames[idx], target_filepath)
75def _get_adapted_dataset(split):
76 """ Gets the adapted dataset for the experiments
77
78 Args :
79 split (str): train or test
80 Returns :
81 (tuple): <training, testing> images and labels
82 """
83 dataset = _get_dataset()
84 key_img = 'x_' + split
85 key_lbl = 'y_' + split
86
87 if split != 'train':
88 dataset[key_img], dataset[key_lbl] = _adapt(dataset[key_img],
89 dataset[key_lbl])
90
91 return (dataset[key_img], dataset[key_lbl])
36def prepare_data():
37 data = io.loadmat(raw_filename)
38 df = pd.DataFrame(dict(
39 spectra=data['Int_ABQR'].tolist(),
40 solute=data['Gamme_ABQR'].ravel(),
41 vial=data['Vial_ABQR'].ravel(),
42 concentration=data['Conc_ABQR'].ravel(),
43 molecule=data['Molecule_ABQR'].ravel()))
44 skf = ShuffleSplit(n_splits=2, test_size=held_out_test_size,
45 random_state=random_state)
46 train_is, test_is = list(skf.split(df))[0]
47 df_train = df.iloc[train_is]
48 df_test = df.iloc[test_is]
49 df_train.to_csv(train_filename, index=False)
50 df_test.to_csv(test_filename, index=False)
9def train_test_split(X, y, train_percentage=0.8):
10 '''
11 Very simple splitting into train and test data. Works for
12 any input shape without dependencies, but is a bit restricted.
13 '''
14 cut_idx = int(floor(X.shape[0] * 0.80))
15 X_train, X_test = X[:cut_idx], X[cut_idx:]
16 y_train, y_test = y[:cut_idx], y[cut_idx:]
17 print("Number of train samples", X_train.shape[0])
18 print("Number of test samples", X_test.shape[0])
19
20 return (X_train, y_train), (X_test, y_test)
82def my_train_split(ds, y):
83 return ds, skorch.dataset.Dataset(corpus.valid[:200], y=None)
35def split_data(df):
36 X = df.drop('Y', axis=1).values
37 y = df['Y'].values
38
39 X_train, X_test, y_train, y_test = train_test_split(
40 X, y, test_size=0.2, random_state=0)
41 data = {"train": {"X": X_train, "y": y_train},
42 "test": {"X": X_test, "y": y_test}}
43 return data

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