I've got a dataset containing a lot of missing values (NAN). I want to use linear or multilinear regression in python and fill all the missing values. You can find the dataset here: Dataset
I have used f_regression(X_train, Y_train) to select which feature should I use.
first of all I convert df['country'] to dummy then used important features then I have used regression but the results Not good.
I have defined following functions to select features and missing values:
def select_features(target,df):
'''Get dataset and terget and print which features are important.'''
df_dummies = pd.get_dummies(df,prefix='',prefix_sep='',drop_first=True)
df_nonan = df_dummies.dropna()
X = df_nonan.drop([target],axis=1)
Y = df_nonan[target]
X = pd.get_dummies(X)
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.30, random_state=40)
f,pval = f_regression(X_train, Y_train)
inds = np.argsort(pval)[::1]
results = pd.DataFrame(np.vstack((f[inds],pval[inds])), columns=X_train.columns[inds], index=['f_values','p_values']).iloc[:,:15]
print(results)
And I have defined following function to predict missing values.
def train(target,features,df,deg=1):
'''Get dataset, target and features and predict nan in target column'''
df_dummies = pd.get_dummies(df,prefix='',prefix_sep='',drop_first=True)
df_nonan = df_dummies[[*features,target]].dropna()
X = df_nonan.drop([target],axis=1)
Y = df_nonan[target]
pol = PolynomialFeatures(degree=deg)
X=X[features]
X = pd.get_dummies(X)
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.40, random_state=40)
X_test, X_val, Y_test, Y_val = train_test_split(X_test, Y_test, test_size=0.50, random_state=40)
# X_train.shape, X_test.shape, Y_train.shape, Y_test.shape
X_train_n = pol.fit_transform(X_train)
reg = linear_model.Lasso()
reg.fit(X_train_n,Y_train);
X_test_n = pol.fit_transform(X_test)
Y_predtrain = reg.predict(X_train_n)
print('train',r2_score(Y_train, Y_predtrain))
Y_pred = reg.predict(X_test_n)
print('test',r2_score(Y_test, Y_pred))
# val
X_val_n = pol.fit_transform(X_val)
X_val_n.shape,X_train_n.shape,X_test_n.shape
Y_valpred = reg.predict(X_val_n)
print('val',r2_score(Y_val, Y_valpred))
X_names = X.columns.values
X_new = df_dummies[X_names].dropna()
X_new = X_new[df_dummies[target].isna()]
X_new_n = pol.fit_transform(X_new)
Y_new = df_dummies.loc[X_new.index,target]
Y_new = reg.predict(X_new_n)
Y_new = pd.Series(Y_new, index=X_new.index)
Y_new.head()
return Y_new, X_names, X_new.index
Then I am using these functions to fill nan for features with p_values<0.05.
But I am not sure is it a good way or not.
With this way many missing remain unpredicted.
Related
I made a random forest regression with filter for y and x variables and I also wanted to add more about shapley values by creating a graph and table with column for the variable and column for the shaply value result. The code plots the graph, but the table is not showing.
So far my code looks like this:
x=widgets.SelectMultiple(
options=list(dataset.select_dtypes('number').columns),
disabled=False,
value=("NUMBER_SPOTS",)
)
def randomforest(y, x):
x = dataset[list(x)]
y = dataset[y]
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)
shap.initjs()
model = RandomForestRegressor(random_state=0).fit(X_train, y_train)
y_predict = model.predict(X_test)
mean_squared_error(y_test, y_predict)**(0.5)
print('Mean Squared Error:', mean_squared_error(y_test, y_predict)**(0.5))
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_train)
shap.summary_plot(shap_values, features=X_train, feature_names=X_train.columns, plot_size=[15,8])
shap_vals = shap_values[0, :]
feature_importance = pd.DataFrame(list(zip(X_train.columns, shap_vals)), columns=['X_train', 'shap_vals'])
feature_importance.sort_values(by=['shap_vals'], ascending=False,inplace=True)
feature_importance
interact(randomforest, y = list(dataset.select_dtypes('number').columns), x = x)
How to use cross validation with sm.GLM (sm = statsmodels.api)?
I am trying to fill the "model" parameter from the cross_val_score. However, since I need to use the sm.GLM I don't know how to use it.
X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.20, random_state = 1)
n = 5
df_cut, bins = pd.cut(X_train, n, retbins=True, right=True)
df_steps = pd.concat([X_train, df_cut, y_train], keys=['age','age_cuts','wage'], axis=1)
# Create dummy variables for the age groups
df_steps_dummies = pd.get_dummies(df_cut)
# Fitting Generalised linear models
fit3 = sm.GLM(df_steps.wage, df_steps_dummies).fit()
# Binning validation set into same 5 bins
bin_mapping = np.digitize(X_test, bins, right=True)
X_valid = pd.get_dummies(bin_mapping)
# Removing any outliers
# X_valid = pd.get_dummies(bin_mapping).drop([6], axis=1)# Prediction
pred2 = fit3.predict(X_valid)
# Calculating RMSE
rms = sqrt(mean_squared_error(y_test, pred2))
print(rms)
scores = cross_val_score(model, X, y, scoring='neg_mean_squared_error',
cv=cv, n_jobs=-1)
Therefore, instead of computing the RMSE like above, I would like to use the cross_val_score. For example, in the model parameter, if I would like to use lasso I would put model = lasso(). However, here I can not put model = sm.GLM().
I hope it is clear...
I wish to follow the below steps:
Load data
Divide into label & feature sets
Normalize data
Divide into test & training sets
Implement oversampling (smote)
Is this the correct order of steps or am I doing anything wrong? I keep getting an error saying "Found input variables with inconsistent numbers of samples: [5114, 3409]".
This error occurs on line: X_train,Y_train = smote.fit_sample(X_train,Y_train)
#data loading
dataset = pd.read_csv('data.csv')
#view data and check for null values
print(dataset.isnull().values.any())
print(dataset.shape)
# Dividing dataset into label and feature sets
X = dataset.drop('Bankrupt?', axis = 1) # Features
Y = dataset['Bankrupt?'] # Labels
print(type(X))
print(type(Y))
print(X.shape)
print(Y.shape)
# Normalizing numerical features so that each feature has mean 0 and variance 1
feature_scaler = StandardScaler()
X_scaled = feature_scaler.fit_transform(X)
# Dividing dataset into training and test sets
X_train, X_test, Y_train, Y_test = train_test_split( X_scaled, Y, test_size = 0.5, random_state = 100)
print(X_train.shape)
print(X_test.shape)
X = dataset.iloc[:,1:].values
y = dataset.iloc[:,0].values.reshape(-1, 1)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
# Implementing Oversampling to balance the dataset;
print("Number of observations in each class before oversampling (training data): \n", pd.Series(Y_train).value_counts())
smote = SMOTE(random_state = 101)
X_train,Y_train = smote.fit_sample(X_train,Y_train)
print("Number of observations in each class after oversampling (training data): \n", pd.Series(Y_train).value_counts())
I am training a model to predict true or false based on some data. I drop the product number from the list of features when training and testing the model.
X = df.drop(columns = 'Product Number', axis = 1)
y = df['result']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)
SVC = LinearSVC(max_iter = 1200)
SVC.fit(X_train, y_train)
y_pred = SVC.predict(X_test)
Is there any way for me to recover the product number and its features for the item that has passed or failed? How do I get/relate the results of y_pred to which product number it corresponds to?
I also plan on using cross validation so the data gets shuffled, would there still be a way for me to recover the product number for each test item?
I realised I'm using cross validation only to evaluate my model's performance so I decided to just run my code without shuffling the data to see the results for each datapoint.
Edit: For evaluation without cross validation, I drop the irrelevant columns only when I pass it to the classifier as shown below:
cols = ['id', 'label']
X = train_data.copy()
y = train_data['label']
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=2)
knn = make_pipeline(StandardScaler(),KNeighborsClassifier(n_neighbors=10))
y_val_pred = knn.fit(X_train.drop(columns=cols), y_train).predict(X_val.drop(columns=cols))
X_val['y_val_pred'] = y_val_pred
I join the y_val_pred after prediction to check which datapoints have been misclassified.
I have a dataset that I have run a K-means algorithm on (scikit-learn), and I want to build a decision tree on each cluster. I can recuperate the values from the cluster, but not the "class" values (I'm doing supervised learning, each element can belong to one of two classes and I need the value associated with the data to build my trees)
Ex: unfiltered data set:
[val1 val2 class]
X_train=[val1 val2]
y_train=[class]
The clustering code is this:
X = clusterDF[clusterDF.columns[clusterDF.columns.str.contains('\'AB\'')]]
y = clusterDF['Class']
(X_train, X_test, y_train, y_test) = train_test_split(X, y,
test_size=0.30)
kmeans = KMeans(n_clusters=3, n_init=5, max_iter=3000, random_state=1)
kmeans.fit(X_train, y_train)
y_pred = kmeans.predict(X_test)
And this is my (unbelievably clunky!) code for extracting the values to build the tree. The issue is the Y values; they aren't consistent with the X values
cl={i: np.where(kmeans.labels_ == i)[0] for i in range(kmeans.n_clusters)}
for j in range(0,len(k_means_labels_unique)):
Xc=None
Y=None
#for i in range(0,len(k_means_labels_unique)):
indexes = cl.get(j,0)
for i, row in X.iterrows():
if i in indexes:
if Xc is not None:
Xc = np.vstack([Xc, [row['first occurrence of \'AB\''],row['similarity to \'AB\'']]])
else:
Xc = np.array([row['first occurrence of \'AB\''],row['similarity to \'AB\'']])
if Y is not None:
Y = np.vstack([Y, y[i]])
else:
Y = np.array(y[i])
Xc = pd.DataFrame(data=Xc, index=range(0, len(X)),
columns=['first occurrence of \'AB\'',
'similarity to \'AB\'']) # 1st row as the column names
Y = pd.DataFrame(data=Y, index=range(0, len(Y)),columns=['Class'])
print("\n\t-----Classifier ", j + 1,"----")
(X_train, X_test, y_train, y_test) = train_test_split(X, Y,
test_size=0.30)
classifier = DecisionTreeClassifier(criterion='entropy',max_depth = 2)
classifier = getResults(
X_train,
y_train,
X_test,
y_test,
classifier,
filename='classif'+str(3 + i),
)
Any ideas (or downright more efficient ways) of taking the clustered data to make a decision tree from?
Did not read all the code but my guess is that passing an index vector into the train_test_split function would help you keep track of the samples.
X = clusterDF[clusterDF.columns[clusterDF.columns.str.contains('\'AB\'')]]
y = clusterDF['Class']
indices = clusterDF.index
X_train, X_test, y_train, y_test, indices_train, indices_test = train_test_split(X, y, indices)