I have a jester data, the data has 100 movies and it's raiting which is given by 24983 user and the data has lots of missing datas. My job is predict its.
I want to start with Decision Tree,
I'm thinking that, First I will select first column of data(it has first movies raitings) and then I will delete first column from data. Then I will fit them, and finally I will found prediction probablity of first column(which is deleted from data)
I'm working on Python
import numpy as np
import pandas as pd
from sklearn.preprocessing import Imputer
from sklearn.preprocessing import PolynomialFeatures
from sklearn.ensemble import RandomForestClassifier
df = pd.read_excel(input_file, header=None)
matrix = df.as_matrix()
imp = Imputer(missing_values=99, strategy='mean', axis=0)
imp.fit(matrix)
matrix= imp.transform(matrix)
train_data = matrix[:,:90] #train data (train data has 90 column)
test_data = matrix[:,90:] #%10 test data (test data has 10 column)
array2 = train_data.copy()
column = array2[:,0] # 0. column should be delete
array2 = np.delete(array2,0,axis=1) # 0. column should be select
clf = RandomForestClassifier(n_estimators=25)
clf.fit(array2.astype(int), column.astype(int))
clf_probs = clf.predict_proba(column)
my last giving error -> ValueError: Number of features of the model must match the input. Model n_features is 89 and input n_features is 24983
I have to predict the column like what I tell you (above the code)
What should I do? I really need help.
Related
I have a dataset like below. I want to do one hot encoding for logistic regression for the 'Item' column. There are 313 distinct items in the 'Item' column I'm getting below error. Can you please assist how to resolve it?
enter image description here
Here is the code:
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [0])],
remainder='passthrough')
X = np.array(ct.fit_transform(X))**
array(<1126x316 sparse matrix of type '<class 'numpy.float64'>'
with 4493 stored elements in Compressed Sparse Row format>, dtype=object)
Use this code, where df is the name of your dataframe
import pandas as pd
df = pd.get_dummies(data = df, columns = ['Item'])
Is it always required to remove the header from an imported CSV for training?
This is what I have...
raw_data_df = [pd.read_csv(
file, header=None, skiprows=[0], low_memory=False) for file in input_files]
train_data_df = pd.concat(raw_data_df)
We used header=None and skiprows=[0] when skipping the header, and we pass it to LogisticRegression().fit()
Or is it better for keeping the header?
If the headers in all files are all equal, then you can keep them. Or you only keep the header of the first file.
The advantage of having a header is that when you run the logistic regression, you can easily find out which coefficients belong to which column names (and so which coefficients are most important).
For example:
import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression()
lr.fit(X_train, y_train)
df_lr_coef = pd.DataFrame({
'features': lr.classes_,
'coefficients': lr.coef_,
'coef_abs': np.abs(lr.coef_),
}).sort_values(by='coef_abs', ascending=False)
I create a program that predict digits from in a dataset. I want when it predict data their should be two cases if it predict right then data should added automatically in dataset otherwise it takes right answer throw user and insert to dataset.
code
import numpy as np
import pandas as pd
import matplotlib.pyplot as pt
from sklearn.tree import DecisionTreeClassifier
data = pd.read_csv("train.csv").values
clf = DecisionTreeClassifier()
xtrain = data[0:21000,1:]
train_label=data[0:21000,0]
clf.fit(xtrain,train_label)
xtest = data[21000: ,1:]
actual_label=data[21000:,0]
d = xtest[9]
d.shape = (28,28)
pt.imshow(d,cmap='gray')
print(clf.predict([xtest[9]]))
pt.show()
I'm not sure I'm following your question, but if you want to distinguish between good and wrong predictions and take different ways, you should specific do that.
predictions = clf.predict(xtest)
good_predictions = xtest[pd.Series(predictions == actual_label)]
bad_predictions = xtest[pd.Series(predictions != actual_label)]
So, in good_predictions will be all the rows in xtest that where predicted right.
I have two different data sets. One for training my classifier and the other one is for testing. Both the datasets are text files with two columns separated by a ",". FIrst column (numbers) is for the independent variable (group) and the second column is for the dependent variable.
Training data set
(just few lines for example. there are no empty lines between each row):
EMI3776438,1
EMI3776438,1
EMI3669492,1
EMI3752004,1
Testing data setup
(as you can see, i have picked data from the training data to be sure that the score surely can't be zero)
EMI3776438,1
Code in Python 3.6:
# #all the import statements have been ignored to keep the code short
# #loading the training data set
training_file_path=r'C:\Users\yyy\Desktop\my files\python\Machine learning\Carepack\modified_columns.txt'
from sklearn import preprocessing
le = preprocessing.LabelEncoder()
training_file_data = pandas.read_table(training_file_path,
header=None,
names=['numbers','group'],
sep=',')
training_file_data = training_file_data.apply(le.fit_transform)
features = ['numbers']
x = training_file_data[features]
y = training_file_data["group"]
from sklearn.model_selection import train_test_split
training_x,testing_x, training_y, testing_y = train_test_split(x, y,
random_state=0,
test_size=0.1)
from sklearn.naive_bayes import GaussianNB
gnb= GaussianNB()
gnb.fit(training_x, training_y)
# #loading the testing data
testing_final_path=r"C:\Users\yyy\Desktop\my files\python\Machine learning\Carepack\testing_final.txt"
testing_sample_data=pandas.read_table(testing_final_path,
sep=',',
header=None,
names=['numbers','group'])
testing_sample_data = testing_sample_data.apply(le.fit_transform)
category = ["numbers"]
testing_sample_data_x = testing_sample_data[category]
# #finding the score of the test data
print(gnb.score(testing_sample_data_x, testing_sample_data["group"]))
First, the above data samples dont show how many classes are there in it. You need to describe more about it.
Secondly, you are calling le.fit_transform again on test data which will forget all the training samples mappings from strings to numbers. The LabelEncoder le will start encoding the test data again from scratch, which will not be equal to how it mapped training data. So the input to GaussianNB is now incorrect and hence incorrect results.
Change that to:
testing_sample_data = testing_sample_data.apply(le.transform)
UPDATE:
I'm sorry I overlooked the fact that you had two columns in your data. LabelEncoder only works on a single column of data. For making it work on multiple pandas columns at once, look at the answers of following question:
Label encoding across multiple columns in scikit-learn
If you are using the latest version of scikit (0.20) or can update to it, then you would not need any such hacks and directly use the OrdinalEncoder:
from sklearn.preprocessing import OrdinalEncoder
enc = OrdinalEncoder()
training_file_data = enc.fit_transform(training_file_data)
And during testing:
training_file_data = enc.transform(training_file_data)
model.summary() prints details of the entire model. Is there a way to just print the last n layer(s) summary only?
If not, can I create a new model from the last n layers of an existing pre-trained model and print its summary instead.
I tried the following but it gives an error probably because of shared inputs:
temp_model = Model(inputs=base_model.layers[-4].input, outputs = base_model.layers[-1].output)
print(temp_model.summary())
Any help will be appreciated.
The last layers from summary are seen as something like this:
You can collect this information piece by piece and then put them together as below:
from collections import defaultdict
import pandas as pd
from tabulate import tabulate
# Number of the last layers
last_layers_len = 5
# Create empty dictionary list
layers_summary = defaultdict(list)
# Iterate over the selected layers
for layer in model.layers[-last_layers_len:]:
layers_summary['Layer'].append(layer.name) # layer name
layers_summary['Output Shape'].append(layer.output_shape) # layer output shape
layers_summary['Param #'].append(layer.count_params()) # layer parameter size
# Convert to pandas dataframe
layers_df = pd.DataFrame.from_dict(layers_summary)
# Tabulate df
print(tabulate(layers_df, headers = 'keys', tablefmt = 'github'))
Output: