I've just seen this answer on SO which shows how to split data using numpy.
Assume we're going to split them as 0.8, 0.1, 0.1 for training, testing, and validation respectively, you do it this way:
train, test, val = np.split(df, [int(.8 * len(df)), int(.9 * len(df))])
I'm interested to know how could I consider stratifying while splitting data using this methodology.
Stratifying is splitting data while keeping the priors of each class you have in data. That is if you're going to take 0.8 for the training set, you take 0.8 from each class you have. Same for test and train.
I tried grouping the data first by class using:
grouped_df = df.groupby(class_col_name, group_keys=False)
But it did not show correct results.
Note: I'm familiar with train_test_split
Simply use your groupby object, grouped_df, which consists of each subsetted data frame where you can then run the needed np.split. Then concatenate all sampled data frames with pd.concat. Atogether, this would stratify according to your quoted message:
train_list = []; test_list = [], val_list = []
grouped_df = df.groupby(class_col_name)
# ITERATE THROUGH EACH SUBSET DF
for i, g in grouped_df:
# STRATIFY THE g (CLASS) DATA FRAME
train, test, val = np.split(g, [int(.8 * len(g)), int(.9 * len(g))])
train_list.append(train); test_list.append(test); val_list.append(val)
final_train = pd.concat(train_list)
final_test = pd.concat(test_list)
final_val = pd.concat(val_list)
Alternatively, a short-hand version using list comprehensions:
# LIST OF ARRAYS
arr_list = [np.split(g, [int(.8 * len(g)), int(.9 * len(g))]) for i, g in grouped_df]
final_train = pd.concat([t[0] for t in arr_list])
final_test = pd.concat([t[1] for t in arr_list])
final_val = pd.concat([v[2] for v in arr_list])
This assumes you have done stratification already such that a "category" column indicates which stratification each entry belongs to.
from collections import namedtuple
Dataset = namedtuple('Dataset', 'train test val')
grouped = df.groupby('headline')
splitted = {x: grouped.get_group(x).sample(frac=1) for x in grouped.groups}
datasets = {k:Dataset(*np.split(df, [int(.8 * len(df)), int(.9 * len(df))])) for k, df in splitted.items()}
This stores each stratified split by the category name assigned in df.
Each item in datasets is a Dataset namedtuple such that training, testing, and validation subsets are accessible by .train, .test, and .val respectively.
Related
I am running some regression models to predict performance.
After running the models I created a variable to see the predictions (y_pred_* are lists with 2567 values):
y_pred_LR = regressor.predict(X_test)
y_pred_SVR = regressor2.predict(X_test)
y_pred_RF = regressor3.predict(X_test)
the types of these prediction lists are Array of float64, while the y_test is a DataFrame.
I wanted to create a table with the results, I tried some different ways, calling as list, trying to convert, trying to select as values, and I did not succeed so far, any one could help?
My last trial was like below:
comparison = pd.DataFrame({'Real': y_test, LR':y_pred_LR,'RF':y_pred_RF,'SVM':y_pred_SVM})
In this case the DataFrame is created but the values don´t appear.
Additionally, I would like to create two new rows with the mean and standard deviation of results and this row should be located at beginning (or first row) of the Data Frame.
Thanks
import pandas as pd
import numpy as np
real = np.array([2] * 10).reshape(-1,1)
y_pred_LR = np.array([0] * 10)
y_pred_SVR = np.array([1] * 10)
y_pred_RF = np.array([5] * 10)
real = real.flatten()
comparison = pd.DataFrame({'real':real,'y_pred_LR':y_pred_LR,'y_pred_SVR':y_pred_SVR,"y_pred_RF":y_pred_RF})
Mean = comparison.mean(axis=0)
StD = comparison.std(axis=0)
Mean_StD = pd.concat([Mean,StD],axis=1).T
result = pd.concat([Mean_StD,comparison],ignore_index=True)
print(result)
I have a house price prediction dataset. I have to split the dataset into train and test.
I would like to know if it is possible to do this by using numpy or scipy?
I cannot use scikit learn at this moment.
I know that your question was only to do a train_test_split with numpy or scipy but there is actually a very simple way to do it with Pandas :
import pandas as pd
# Shuffle your dataset
shuffle_df = df.sample(frac=1)
# Define a size for your train set
train_size = int(0.7 * len(df))
# Split your dataset
train_set = shuffle_df[:train_size]
test_set = shuffle_df[train_size:]
For those who would like a fast and easy solution.
Although this is old question, this answer might help.
This is how sklearn implements train_test_split, this method given below, takes similar arguments as sklearn.
import numpy as np
from itertools import chain
def _indexing(x, indices):
"""
:param x: array from which indices has to be fetched
:param indices: indices to be fetched
:return: sub-array from given array and indices
"""
# np array indexing
if hasattr(x, 'shape'):
return x[indices]
# list indexing
return [x[idx] for idx in indices]
def train_test_split(*arrays, test_size=0.25, shufffle=True, random_seed=1):
"""
splits array into train and test data.
:param arrays: arrays to split in train and test
:param test_size: size of test set in range (0,1)
:param shufffle: whether to shuffle arrays or not
:param random_seed: random seed value
:return: return 2*len(arrays) divided into train ans test
"""
# checks
assert 0 < test_size < 1
assert len(arrays) > 0
length = len(arrays[0])
for i in arrays:
assert len(i) == length
n_test = int(np.ceil(length*test_size))
n_train = length - n_test
if shufffle:
perm = np.random.RandomState(random_seed).permutation(length)
test_indices = perm[:n_test]
train_indices = perm[n_test:]
else:
train_indices = np.arange(n_train)
test_indices = np.arange(n_train, length)
return list(chain.from_iterable((_indexing(x, train_indices), _indexing(x, test_indices)) for x in arrays))
Of course sklearn's implementation supports stratified k-fold, splitting of pandas series etc. This one only works for splitting lists and numpy arrays, which I think will work for your case.
This solution using pandas and numpy only
def split_train_valid_test(data,valid_ratio,test_ratio):
shuffled_indcies=np.random.permutation(len(data))
valid_set_size= int(len(data)*valid_ratio)
valid_indcies=shuffled_indcies[:valid_set_size]
test_set_size= int(len(data)*test_ratio)
test_indcies=shuffled_indcies[valid_set_size:test_set_size+valid_set_size]
train_indices=shuffled_indcies[test_set_size:]
return data.iloc[train_indices],data.iloc[valid_indcies],data.iloc[test_indcies]
train_set,valid_set,test_set=split_train_valid_test(dataset,valid_ratio=0.2,test_ratio=0.2)
print(len(train_set),len(valid_set),len(test_set))
##out: (16512, 4128, 4128)
This code should work (Assuming X_data is a pandas DataFrame):
import numpy as np
num_of_rows = len(X_data) * 0.8
values = X_data.values
np.random_shuffle(values) #shuffles data to make it random
train_data = values[:num_of_rows] #indexes rows for training data
test_data = values[num_of_rows:] #indexes rows for test data
Hope this helps!
import numpy as np
import pandas as pd
X_data = pd.read_csv('house.csv')
Y_data = X_data["prices"]
X_data.drop(["offers", "brick", "bathrooms", "prices"],
axis=1, inplace=True) # important to drop prices as well
# create random train/test split
indices = range(X_data.shape[0])
num_training_instances = int(0.8 * X_data.shape[0])
np.random.shuffle(indices)
train_indices = indices[:num_training_indices]
test_indices = indices[num_training_indices:]
# split the actual data
X_data_train, X_data_test = X_data.iloc[train_indices], X_data.iloc[test_indices]
Y_data_train, Y_data_test = Y_data.iloc[train_indices], Y_data.iloc[test_indices]
This assumes you want a random split. What happens is that we're creating a list of indices as long as the number of data points you have, i.e. the first axis of X_data (or Y_data). We then put them in random order and just take the first 80% of those random indices as training data and the rest for testing. [:num_training_indices] just selects the first num_training_indices from the list. After that you just extract the rows from your data using the lists of random indices and your data is split. Remember to drop the prices from your X_data and to set a seed if you want the split to be reproducible (np.random.seed(some_integer) in the beginning).
This is a follow on question from Subsetting Dask DataFrames. I wish to shuffle data from a dask dataframe before sending it in batches to a ML algorithm.
The answer in that question was to do the following:
for part in df.repartition(npartitions=100).to_delayed():
batch = part.compute()
However, even if I was to shuffle the contents of batch I'm a bit worried that it might not be ideal. The data is a time series set so datapoints would be highly correlated within each partition.
What I would ideally like is something along the lines of:
rand_idx = np.random.choice(len(df), batch_size, replace=False)
batch = df.iloc[rand_idx, :]
which would work on pandas but not dask. Any thoughts?
Edit 1: Potential Solution
I tried doing
train_len = int(len_df*0.8)
idx = np.random.permutation(len_df)
train_idx = idx[:train_len]
test_idx = idx[train_len:]
train_df = df.loc[train_idx]
test_df = df.loc[test_idx]
However, if I try doing train_df.loc[:5,:].compute() this return a 124451 row dataframe. So clearly using dask wrong.
I recommend adding a column of random data to your dataframe and then using that to set the index:
df = df.map_partitions(add_random_column_to_pandas_dataframe, ...)
df = df.set_index('name-of-random-column')
I encountered the same issue recently and came up with a different approach using dask array and shuffle_slice introduced in this pull request
It shuffles the whole sample
import numpy as np
from dask.array.slicing import shuffle_slice
d_arr = df.to_dask_array(True)
df_len = len(df)
np.random.seed(42)
index = np.random.choice(df_len, df_len, replace=False)
d_arr = shuffle_slice(d_arr, index)
and to transform back to dask dataframe
df = d_arr.to_dask_dataframe(df.columns)
for me it works well for large data sets
If you're trying to separate your dataframe into training and testing subsets, it is what does sklearn.model_selection.train_test_split and it works with pandas.DataFrame. (Go there for an example)
And for your case of using it with dask, you may be interested by the library dklearn, that seems to implements this function.
To do that, we can use the train_test_split function, which mirrors
the scikit-learn function of the same name. We'll hold back 20% of the
rows:
from dklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.2)
More information here.
Note: I did not perform any test with dklearn, this is just a thing I came across, but I hope it can help.
EDIT: what about dask.DataFrame.random_split?
Examples
50/50 split
>>> a, b = df.random_split([0.5, 0.5])
80/10/10 split, consistent random_state
>>> a, b, c = df.random_split([0.8, 0.1, 0.1], random_state=123)
Use for ML applications is illustrated here
For people here really just wanting to shuffle the rows as the title implies:
This is costly
import numpy as np
random_idx = np.random.permutation(len(sd.index))
sd.assign(random_idx=random_idx)
sd = sd.set_index('x', sorted=True)
I use scikit linear regression and if I change the order of the features, the coef are still printed in the same order, hence I would like to know the mapping of the feature with the coeff.
#training the model
model_1_features = ['sqft_living', 'bathrooms', 'bedrooms', 'lat', 'long']
model_2_features = model_1_features + ['bed_bath_rooms']
model_3_features = model_2_features + ['bedrooms_squared', 'log_sqft_living', 'lat_plus_long']
model_1 = linear_model.LinearRegression()
model_1.fit(train_data[model_1_features], train_data['price'])
model_2 = linear_model.LinearRegression()
model_2.fit(train_data[model_2_features], train_data['price'])
model_3 = linear_model.LinearRegression()
model_3.fit(train_data[model_3_features], train_data['price'])
# extracting the coef
print model_1.coef_
print model_2.coef_
print model_3.coef_
The trick is that right after you have trained your model, you know the order of the coefficients:
model_1 = linear_model.LinearRegression()
model_1.fit(train_data[model_1_features], train_data['price'])
print(list(zip(model_1.coef_, model_1_features)))
This will print the coefficients and the correct feature. (Tested with pandas DataFrame)
If you want to reuse the coefficients later you can also put them in a dictionary:
coef_dict = {}
for coef, feat in zip(model_1.coef_,model_1_features):
coef_dict[feat] = coef
(You can test it for yourself by training two models with the same features but, as you said, shuffled order of features.)
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
coef_table = pd.DataFrame(list(X_train.columns)).copy()
coef_table.insert(len(coef_table.columns),"Coefs",regressor.coef_.transpose())
#Robin posted a great answer, but for me I had to make one tweak on it to work the way I wanted, and it was to refer to the dimension of the 'coef_' np.array that I wanted, namely modifying to this: model_1.coef_[0,:], as below:
coef_dict = {}
for coef, feat in zip(model_1.coef_[0,:],model_1_features):
coef_dict[feat] = coef
Then the dict was created as I pictured it, with {'feature_name' : coefficient_value} pairs.
Here is what I use for pretty printing of coefficients in Jupyter. I'm not sure I follow why order is an issue - as far as I know the order of the coefficients should match the order of the input data that you gave it.
Note that the first line assumes you have a Pandas data frame called df in which you originally stored the data prior to turning it into a numpy array for regression:
fieldList = np.array(list(df)).reshape(-1,1)
coeffs = np.reshape(np.round(clf.coef_,5),(-1,1))
coeffs=np.concatenate((fieldList,coeffs),axis=1)
print(pd.DataFrame(coeffs,columns=['Field','Coeff']))
Borrowing from Robin, but simplifying the syntax:
coef_dict = dict(zip(model_1_features, model_1.coef_))
Important note about zip: zip assumes its inputs are of equal length, making it especially important to confirm that the lengths of the features and coefficients match (which in more complicated models might not be the case). If one input is longer than the other, the longer input will have the values in its extra index positions cut off. Notice the missing 7 in the following example:
In [1]: [i for i in zip([1, 2, 3], [4, 5, 6, 7])]
Out[1]: [(1, 4), (2, 5), (3, 6)]
pd.DataFrame(data=regression.coef_, index=X_train.columns)
All of these answers were great but what personally worked for me was this, as the feature names I needed were the columns of my train_date dataframe:
pd.DataFrame(data=model_1.coef_,columns=train_data.columns)
Right after training the model, the coefficient values are stored in the variable model.coef_[0]. We can iterate over the column names and store the column name and their coefficient value in a dictionary.
model.fit(X_train,y)
# assuming all the columns except last one is used in training
columns = data.iloc[:,-1].columns
coef_dict = {}
for i in range(0,len(columns)):
coef_dict[columns[i]] = model.coef_[0][i]
Hope this helps!
As of scikit-learn version 1.0, the LinearRegression estimator has a feature_names_in_ attribute. From the docs:
feature_names_in_ : ndarray of shape (n_features_in_,)
Names of features seen during fit. Defined only when X has feature names that are all strings.
New in version 1.0.
Assuming you're fitting on a pandas.DataFrame (train_data), your estimators (model_1, model_2, and model_3) will have the attribute. You can line up your coefficients using any of the methods listed in previous answers, but I'm in favor of this one:
coef_series = pd.Series(
data=model_1.coef_,
index=model_1.feature_names_in_
)
A minimally reproducible example
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
# for repeatability
np.random.seed(0)
# random data
Xy = pd.DataFrame(
data=np.random.random((10, 3)),
columns=["x0", "x1", "y"]
)
# separate X and y
X = Xy.drop(columns="y")
y = Xy.y
# initialize estimator
lr = LinearRegression()
# fit to pandas.DataFrame
lr.fit(X, y)
# get coeficients and their respective feature names
coef_series = pd.Series(
data=lr.coef_,
index=lr.feature_names_in_
)
print(coef_series)
x0 0.230524
x1 -0.275611
dtype: float64
I'm loading a csv, using Numpy, as a dataset to create a decision tree model in Python. using the below extract places columns 0-7 in X and the last column as the target in Y.
#load and set data
data = np.loadtxt("data/tmp.csv", delimiter=",")
X = data[:,0:7] #identify columns as data sets
Y = data[:,8] #identfy last column as target
#create model
clf = tree.DecisionTreeClassifier()
clf = clf.fit(X, Y)
What i'd like to know is if its possible to have the classifier in any column. for example if its in the fourth column would the following code still fit the model correctly or would it produce errors when it comes to predicting?
#load and set data
data = np.loadtxt("data/tmp.csv", delimiter=",")
X = data[:,0:8] #identify columns as data sets
Y = data[:,3] #identfy fourth column as target
#create model
clf = tree.DecisionTreeClassifier()
clf = clf.fit(X, Y)
If you have >4 columns, and the 4th one is the target and the others are features, here's one way (out of many) to load them:
# load data
X = np.hstack([data[:, :3], data[:, 5:]]) # features
Y = data[:,4] # target
# process X & Y
(with belated thanks to #omerbp for reminding me hstack takes a tuple/list, not naked arguments!)
First of all, As suggested by #mescalinum in a comment to the question, think of this situation:
.... 4th_feature ... label
.... 1 ... 1
.... 0 ... 0
.... 1 ... 1
............................
In this example, the classifier (any classifier, not DecisionTreeClassifier particularly) will learn that the 4th feature can best predict the label, since the 4th feature is the label. Unfortunately, this issue happen a lot (by accident I mean).
Secondly, if you want the 4th feature to be input label, you can just swap the columns:
arr[:,[frm, to]] = arr[:,[to, frm]]
#Ahemed Fasih's answer can also do the trick, however its around 10 time slower:
import timeit
setup_code = """
import numpy as np
i, j = 400000, 200
my_array = np.arange(i*j).reshape(i, j)
"""
swap_cols = """
def swap_cols(arr, frm, to):
arr[:,[frm, to]] = arr[:,[to, frm]]
"""
stack ="np.hstack([my_array[:, :3], my_array[:, 5:]])"
swap ="swap_cols(my_array, 4, 8)"
print "hstack - total time:", min(timeit.repeat(stmt=stack,setup=setup_code,number=20,repeat=3))
#hstack - total time: 3.29988478635
print "swap - total time:", min(timeit.repeat(stmt=swap,setup=setup_code+swap_cols,number=20,repeat=3))
#swap - total time: 0.372791106328