def basic_test(backend='ludwig',
use_gpu=True,
ignore_columns=[],
run_extra=False):
if run_extra:
for py_file in [
x for x in os.listdir('../functional_testing') if '.py' in x
]:
os.system(f'python3 ../functional_testing/{py_file}')
# Create & Learn
mdb = Predictor(name='home_rentals_price')
mdb.learn(
to_predict='rental_price',
from_data=
"https://s3.eu-west-2.amazonaws.com/mindsdb-example-data/home_rentals.csv",
backend=backend,
stop_training_in_x_seconds=20,
use_gpu=use_gpu)
# Reload & Predict
mdb = Predictor(name='home_rentals_price')
prediction = mdb.predict(when={'sqft': 300}, use_gpu=use_gpu)
# Test all different forms of output
# No need to print them, we're just doing so for debugging purposes, we just want to see if the interface will crash or not
print(prediction)
print(prediction[0])
for item in prediction:
print(item)
print(type(list(prediction.evaluations.values())[0][0]))
assert ('ProbabilityEvaluation'
in str(type(list(prediction.evaluations.values())[0][0])))
for p in prediction:
print(p)
print(prediction[0].as_dict())
print(prediction[0].as_list())
print(prediction[0]['rental_price_confidence'])
print(type(prediction[0]['rental_price_confidence']))
print('\n\n========================\n\n')
print(prediction[0].explain())
print('\n\n')
# See if we can get the adapted metadata
amd = mdb.get_model_data('home_rentals_price')
# Make some simple assertions about it
assert (5 < len(list(amd.keys())))
from mindsdb import Predictor
# We tell mindsDB what we want to learn and from what data
mdb = Predictor(name='home_rentals_price')
mdb.learn(
to_predict=
'rental_price', # the column we want to learn to predict given all the data in the file
from_data=
"https://s3.eu-west-2.amazonaws.com/mindsdb-example-data/home_rentals.csv"
# the path to the file where we can learn from, (note: can be url)
)
prediction = mdb.predict(when={'sqft': 300})
print(prediction[0])
amd = mdb.get_model_data('home_rentals_price')
print(amd)
from mindsdb import Predictor
import sys
import pandas as pd
mdb = Predictor(name='sensor123')
mdb.learn(
to_predict='rental_price',
from_data=
"https://mindsdb-example-data.s3.eu-west-2.amazonaws.com/home_rentals.csv",
use_gpu=True,
stop_training_in_x_seconds=15)
p_arr = mdb.predict(
when_data=
'https://mindsdb-example-data.s3.eu-west-2.amazonaws.com/home_rentals.csv')
for p in p_arr:
exp_s = p.epitomize()
#exp = p.explain()
#print(exp)
print(exp_s)
print(mdb.get_model_data('sensor123'))
import sys
import pandas as pd
import json
import time
mdb = Predictor(name='test_predictor')
#'rental_price',
mdb.learn(
to_predict=['neighborhood'],
from_data=
"https://mindsdb-example-data.s3.eu-west-2.amazonaws.com/home_rentals.csv",
use_gpu=True,
stop_training_in_x_seconds=33,
backend='lightwood',
unstable_parameters_dict={'use_selfaware_model': False})
p_arr = mdb.predict(
when_data=
'https://mindsdb-example-data.s3.eu-west-2.amazonaws.com/home_rentals.csv')
#print(mdb.predict(when={'number_of_rooms': 3, 'number_of_bathrooms': 2, 'neighborhood': 'south_side', 'sqft':2411}, run_confidence_variation_analysis=True)[0].explain())
print(mdb.get_model_data('test_predictor'))
exit()
for p in p_arr:
exp_s = p.epitomize()
exp = p.explain()
print(exp)
print(exp_s)
exit()