Python plot showing no output - Powerbi

Anonymous — Mon, 09 Sep 2019 11:10:59 GMT

Hi guys,

I'm a bit new in python but my code is working on spyder or jupyter notebook.

The equivalent script I'm running on PowerBi is the following :

Below is the code I used to import my data.

import pandas as pd
import numpy as np
from sklearn import metrics

from sklearn.model_selection import StratifiedShuffleSplit,StratifiedKFold
from sklearn.model_selection import LeavePGroupsOut
from sklearn.model_selection import GroupKFold
from sklearn.pipeline import Pipeline
import random

from sklearn import tree
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
from sklearn import metrics


df_order_t = pd.read_csv('C:/Users/morgan.quezel-ambrun/Documents/Stage_MBA/order_products_train.csv', dtype={
            'order_id': np.int32,
            'product_id': np.uint16,
            'add_to_cart_order': np.int16,
            'reordered': np.int8}, engine='python')
products= pd.read_csv('C:/Users/morgan.quezel-ambrun/Documents/Stage_MBA/products.csv',dtype={
        'product_id': np.uint16,
        'product_name': np.str,
        'aisle_id': np.uint8,
        'department_id': np.uint8})

orders = pd.read_csv('C:/Users/morgan.quezel-ambrun/Documents/Stage_MBA/orders.csv',dtype={
        'order_id': np.int32,
        'user_id': np.int32,
        'eval_set': 'category',
        'order_number': np.int16,
        'order_dow': np.int8,
        'order_hour_of_day': np.int8,
        'days_since_prior_order': np.float32})

aisles = pd.read_csv('C:/Users/morgan.quezel-ambrun/Documents/Stage_MBA/aisles.csv',dtype={
        'aisle_id': np.uint8,
        'aisle': np.str})

departments = pd.read_csv('C:/Users/morgan.quezel-ambrun/Documents/Stage_MBA/departments.csv',dtype={
        'department_id': np.uint8,
        'department':np.str})

orders_train = orders.loc[orders['eval_set'] == 'train']
merged_ = pd.merge(df_order_t,orders_train,on='order_id',how='left')
del merged_['eval_set']
del merged_['order_id']

Below the library used:

import pandas as pd
import numpy as np
from sklearn import metrics

from sklearn.model_selection import StratifiedShuffleSplit,StratifiedKFold
from sklearn.model_selection import LeavePGroupsOut
from sklearn.model_selection import GroupKFold
from sklearn.pipeline import Pipeline
import random

from sklearn import tree

The script: Basically I am creating a plot of my accuracy score depending on the depth of my tree for my training set and validation set.

# The following code to create a dataframe and remove duplicated rows is always executed and acts as a preamble for your script:
# dataset = pandas.DataFrame(add_to_cart_order, days_since_prior_order, order_dow, order_hour_of_day, order_number, product_id, reordered, user_id)
# dataset = dataset.drop_duplicates()
# Paste or type your script code here:

X = dataset.values
y = dataset.reordered.values

groups = dataset.user_id.values
group_kfold = GroupKFold(n_splits=5)
group_kfold.get_n_splits(X, y, groups)

for train_index, test_index in group_kfold.split(X, y, groups):
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]

X_train = pd.DataFrame(X_train.astype(int), columns=['product_id',
'add_to_cart_order',
'reordered',
'user_id',
'order_number',
'order_dow',
'order_hour_of_day',
'days_since_prior_order'])
X_train = X_train.drop('reordered', 1)
X_train = X_train.drop('user_id',1)
X_train = X_train.drop("product_id",1)

X_test = pd.DataFrame(X_test.astype(int), columns=['product_id',
'add_to_cart_order',
'reordered',
'user_id',
'order_number',
'order_dow',
'order_hour_of_day',
'days_since_prior_order'])
X_test = X_test.drop('reordered', 1)
X_test = X_test.drop('user_id',1)
X_test = X_test.drop("product_id",1)

y_train = pd.DataFrame(y_train,columns=['reordered'])
y_test = pd.DataFrame(y_test,columns=['reordered'])

max_depths = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]
training_accuracies = []
testing_accuracies = []

for max_depth in max_depths:

    dtc = tree.DecisionTreeClassifier(criterion="entropy", max_depth=max_depth, min_samples_split=0.05)
    dtc_fit = dtc.fit(X_train,y_train)

    prediction_training = dtc_fit.predict(X_train)
    training_accuracy = dtc_fit.score(X_train, y_train)
    training_accuracies.append(training_accuracy)

    prediction_testing = dtc_fit.predict(X_test)
    testing_accuracy = dtc_fit.score(X_test, y_test)
    testing_accuracies.append(testing_accuracy)

import matplotlib.pyplot as plt
plt.plot(max_depths, training_accuracies, max_depths, testing_accuracies,linewidth=2.0)

plt.title('Accuracy Score', fontsize = 20)
plt.xlabel("Max depth", fontsize=20)
plt.ylabel("Accuracy", fontsize = 20)
plt.legend(['Validation set', 'Train set'], loc='upper right')

plt.show()

My output should be:

I have two issues with Powerbi.

My output is weird as I have the following results:

If I want to specify a bit more where my axis starts (STEP I NEED , to properly visualise my graph)

plt.axis([0, 21, 0.58, 0.7])

My output turns to be empty

I have no message of error...

Thank you a lot for your help

topic Python plot showing no output - Powerbi in Developer

Python plot showing no output - Powerbi