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from rasa_core.actions import Action
from rasa_core.events import SlotSet
from IPython.core.display import Image, display

import requests

class ApiAction(Action):
    def name(self):
        return "action_retrieve_image"

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from rasa_core.policies import FallbackPolicy, KerasPolicy, MemoizationPolicy
from rasa_core.agent import Agent

# The fallback action will be executed if the intent recognition has #a confidence below nlu_threshold or if none of the dialogue #policies predict an action with confidence higher than #core_threshold.

fallback = FallbackPolicy(fallback_action_name="utter_unclear",
                          core_threshold=0.2,
                          nlu_threshold=0.1)

agent = Agent('domain.yml', policies=[MemoizationPolicy(), KerasPolicy(), fallback])

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from rasa_core.channels.slack import SlackInput
from rasa_core.agent import Agent
from rasa_core.interpreter import RasaNLUInterpreter
import yaml
from rasa_core.utils import EndpointConfig


nlu_interpreter = RasaNLUInterpreter('./models/nlu/default/current')
action_endpoint = EndpointConfig(url="http://localhost:5055/webhook")
agent = Agent.load('./models/dialogue', interpreter = nlu_interpreter, action_endpoint = action_endpoint)

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from rasa_core.channels.slack import SlackInput
from rasa_core.agent import Agent
from rasa_core.interpreter import RasaNLUInterpreter
import yaml
from rasa_core.utils import EndpointConfig


nlu_interpreter = RasaNLUInterpreter('./models/nlu/default/current')
action_endpoint = EndpointConfig(url="http://localhost:5055/webhook")
agent = Agent.load('./models/dialogue', interpreter = nlu_interpreter, action_endpoint = action_endpoint)

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articles = pd.DataFrame({
  'holiday': 'Article_Published',
  'ds': pd.to_datetime(['2018-07-02', '2018-07-06', '2018-07-08',
                        '2018-07-09', '2018-07-12', '2018-07-19', '2018-07-26', '2018-07-31',
                        '2018-08-06', '2018-08-15', '2018-07-19', '2018-08-26', '2018-08-31',
                        '2018-09-01', '2018-09-04', '2018-09-11', '2018-09-17', '2018-09-23',
                        '2018-10-02', '2018-10-09', '2018-10-18', '2018-10-19', '2018-10-26',
                        '2018-11-02', '2018-11-08', '2018-11-24', '2018-12-05', '2018-12-13',
                        '2018-12-19', '2018-12-24', '2018-12-27', '2019-01-08', '2019-01-11',
                        '2019-01-22', '2019-01-24', '2019-01-28', '2019-02-01', '2019-02-04',

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Evaluated Logistic Regression, Random Forest models with 3 folds and AuPR metric.
Evaluated 3 Logistic Regression models with AuPR between [0.6751930383321765, 0.7768725281794376]
Evaluated 16 Random Forest models with AuPR between [0.7781671467343991, 0.8104798040316159]

Selected model Random Forest classifier with parameters:
|-----------------------|--------------|
| Model Param           |     Value    |
|-----------------------|--------------|
| modelType             | RandomForest |
| featureSubsetStrategy |         auto |

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# library
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns

# Get the data (csv file is hosted on the web)
url = 'https://python-graph-gallery.com/wp-content/uploads/volcano.csv'
data = pd.read_csv(url)