





# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets




def    A_(			A   ):
	return 1 / (1 + np.exp(-z   ))




def    A_(			A				,      A   ):
	return (-y * np.log(A   ) - (1 - y) * np.log(1 - h   )).mean()




def    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     np.dot(A				,      A   )
	return np.sum(y * scores - np.log(1 + np.exp(A   )   )   )




def    A_(			A				,      A				,      A				,      A=70000   ):
	UpperCAmelCase_								=     np.zeros(x.shape[1]   )

	for iterations in range(A   ):
		UpperCAmelCase_								=     np.dot(A				,      A   )
		UpperCAmelCase_								=     sigmoid_function(A   )
		UpperCAmelCase_								=     np.dot(x.T				,      h - y   ) / y.size
		UpperCAmelCase_								=     theta - alpha * gradient  # updating the weights
		UpperCAmelCase_								=     np.dot(A				,      A   )
		UpperCAmelCase_								=     sigmoid_function(A   )
		UpperCAmelCase_								=     cost_function(A				,      A   )
		if iterations % 100 == 0:
			print(f"""loss: {j} \t"""   )  # printing the loss after every 100 iterations
	return theta


# In[68]:

if __name__ == "__main__":
				UpperCamelCase__   : List[str]					  =							datasets.load_iris()
				UpperCamelCase__   : str					  =							iris.data[:, :2]
				UpperCamelCase__   : Dict					  =							(iris.target != 0) * 1

				UpperCamelCase__   : int					  =							0.1
				UpperCamelCase__   : Any					  =							logistic_reg(alpha, x, y, max_iterations=7_00_00)
				print("""theta: """, theta)  # printing the theta i.e our weights vector




				def    A_(			A   ):
					return sigmoid_function(
					    np.dot(A				,      A   )   )  # predicting the value of probability from the logistic regression algorithm

				plt.figure(figsize=(10, 6))
				plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color="""b""", label="""0""")
				plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color="""r""", label="""1""")
				(UpperCamelCase__)   : Tuple					  =							(x[:, 0].min(), x[:, 0].max())
				(UpperCamelCase__)   : List[str]					  =							(x[:, 1].min(), x[:, 1].max())
				(UpperCamelCase__)   : Tuple					  =							np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
				UpperCamelCase__   : str					  =							np.c_[xxa.ravel(), xxa.ravel()]
				UpperCamelCase__   : Tuple					  =							predict_prob(grid).reshape(xxa.shape)
				plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="""black""")

				plt.legend()
				plt.show()









from __future__ import annotations

import unittest

from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow


if is_tf_available():
				from transformers import (
				    AutoConfig,
				    BertConfig,
				    GPTaConfig,
				    TaConfig,
				    TFAutoModel,
				    TFAutoModelForCausalLM,
				    TFAutoModelForMaskedLM,
				    TFAutoModelForPreTraining,
				    TFAutoModelForQuestionAnswering,
				    TFAutoModelForSeqaSeqLM,
				    TFAutoModelForSequenceClassification,
				    TFAutoModelWithLMHead,
				    TFBertForMaskedLM,
				    TFBertForPreTraining,
				    TFBertForQuestionAnswering,
				    TFBertForSequenceClassification,
				    TFBertModel,
				    TFGPTaLMHeadModel,
				    TFRobertaForMaskedLM,
				    TFTaForConditionalGeneration,
				)
				from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
				from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
				from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST

if is_torch_available():
				from transformers import (
				    AutoModel,
				    AutoModelForCausalLM,
				    AutoModelForMaskedLM,
				    AutoModelForPreTraining,
				    AutoModelForQuestionAnswering,
				    AutoModelForSeqaSeqLM,
				    AutoModelForSequenceClassification,
				    AutoModelWithLMHead,
				    BertForMaskedLM,
				    BertForPreTraining,
				    BertForQuestionAnswering,
				    BertForSequenceClassification,
				    BertModel,
				    GPTaLMHeadModel,
				    RobertaForMaskedLM,
				    TaForConditionalGeneration,
				)







@is_pt_tf_cross_test
class   _UpperCamelCase  (       unittest.TestCase  ):







					'''simple docstring'''

					@slow
					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						for model_name in ["bert-base-uncased"]:
							UpperCAmelCase_								=     AutoConfig.from_pretrained(__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)

							UpperCAmelCase_								=     TFAutoModel.from_pretrained(__lowercase ,					from_pt=__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)

							UpperCAmelCase_								=     AutoModel.from_pretrained(__lowercase ,					from_tf=__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)





					@slow
					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						for model_name in ["bert-base-uncased"]:
							UpperCAmelCase_								=     AutoConfig.from_pretrained(__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)

							UpperCAmelCase_								=     TFAutoModelForPreTraining.from_pretrained(__lowercase ,					from_pt=__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)

							UpperCAmelCase_								=     AutoModelForPreTraining.from_pretrained(__lowercase ,					from_tf=__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)





					@slow
					def        SCREAMING_SNAKE_CASE							(						self    :  Any		):






						'''simple docstring'''







						for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							UpperCAmelCase_								=     AutoConfig.from_pretrained(__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)

							UpperCAmelCase_								=     TFAutoModelForCausalLM.from_pretrained(__lowercase ,					from_pt=__lowercase		)
							UpperCAmelCase_						,	UpperCAmelCase_								=     TFAutoModelForCausalLM.from_pretrained(
							    __lowercase ,					output_loading_info=__lowercase ,					from_pt=__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)

							UpperCAmelCase_								=     AutoModelForCausalLM.from_pretrained(__lowercase ,					from_tf=__lowercase		)
							UpperCAmelCase_						,	UpperCAmelCase_								=     AutoModelForCausalLM.from_pretrained(
							    __lowercase ,					output_loading_info=__lowercase ,					from_tf=__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)





					@slow
					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any]		):






						'''simple docstring'''







						for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							UpperCAmelCase_								=     AutoConfig.from_pretrained(__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)

							UpperCAmelCase_								=     TFAutoModelWithLMHead.from_pretrained(__lowercase ,					from_pt=__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)

							UpperCAmelCase_								=     AutoModelWithLMHead.from_pretrained(__lowercase ,					from_tf=__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)





					@slow
					def        SCREAMING_SNAKE_CASE							(						self    :  str		):






						'''simple docstring'''







						for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							UpperCAmelCase_								=     AutoConfig.from_pretrained(__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)

							UpperCAmelCase_								=     TFAutoModelForMaskedLM.from_pretrained(__lowercase ,					from_pt=__lowercase		)
							UpperCAmelCase_						,	UpperCAmelCase_								=     TFAutoModelForMaskedLM.from_pretrained(
							    __lowercase ,					output_loading_info=__lowercase ,					from_pt=__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)

							UpperCAmelCase_								=     AutoModelForMaskedLM.from_pretrained(__lowercase ,					from_tf=__lowercase		)
							UpperCAmelCase_						,	UpperCAmelCase_								=     AutoModelForMaskedLM.from_pretrained(
							    __lowercase ,					output_loading_info=__lowercase ,					from_tf=__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)





					@slow
					def        SCREAMING_SNAKE_CASE							(						self    :  Union[str, Any]		):






						'''simple docstring'''







						for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							UpperCAmelCase_								=     AutoConfig.from_pretrained(__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)

							UpperCAmelCase_								=     TFAutoModelForSeqaSeqLM.from_pretrained(__lowercase ,					from_pt=__lowercase		)
							UpperCAmelCase_						,	UpperCAmelCase_								=     TFAutoModelForSeqaSeqLM.from_pretrained(
							    __lowercase ,					output_loading_info=__lowercase ,					from_pt=__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)

							UpperCAmelCase_								=     AutoModelForSeqaSeqLM.from_pretrained(__lowercase ,					from_tf=__lowercase		)
							UpperCAmelCase_						,	UpperCAmelCase_								=     AutoModelForSeqaSeqLM.from_pretrained(
							    __lowercase ,					output_loading_info=__lowercase ,					from_tf=__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)





					@slow
					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[int]		):






						'''simple docstring'''







						for model_name in ["bert-base-uncased"]:
							UpperCAmelCase_								=     AutoConfig.from_pretrained(__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)

							UpperCAmelCase_								=     TFAutoModelForSequenceClassification.from_pretrained(__lowercase ,					from_pt=__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)

							UpperCAmelCase_								=     AutoModelForSequenceClassification.from_pretrained(__lowercase ,					from_tf=__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)





					@slow
					def        SCREAMING_SNAKE_CASE							(						self    :  str		):






						'''simple docstring'''







						for model_name in ["bert-base-uncased"]:
							UpperCAmelCase_								=     AutoConfig.from_pretrained(__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)

							UpperCAmelCase_								=     TFAutoModelForQuestionAnswering.from_pretrained(__lowercase ,					from_pt=__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)

							UpperCAmelCase_								=     AutoModelForQuestionAnswering.from_pretrained(__lowercase ,					from_tf=__lowercase		)
							self.assertIsNotNone(__lowercase		)
							self.assertIsInstance(__lowercase ,					__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Tuple		):






						'''simple docstring'''







						UpperCAmelCase_								=     TFAutoModelWithLMHead.from_pretrained(__lowercase ,					from_pt=__lowercase		)
						self.assertIsInstance(__lowercase ,					__lowercase		)
						self.assertEqual(model.num_parameters() ,					1_44_10		)
						self.assertEqual(model.num_parameters(only_trainable=__lowercase		) ,					1_44_10		)

						UpperCAmelCase_								=     AutoModelWithLMHead.from_pretrained(__lowercase ,					from_tf=__lowercase		)
						self.assertIsInstance(__lowercase ,					__lowercase		)
						self.assertEqual(model.num_parameters() ,					1_44_10		)
						self.assertEqual(model.num_parameters(only_trainable=__lowercase		) ,					1_44_10		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     TFAutoModelWithLMHead.from_pretrained(__lowercase ,					from_pt=__lowercase		)
						self.assertIsInstance(__lowercase ,					__lowercase		)
						self.assertEqual(model.num_parameters() ,					1_44_10		)
						self.assertEqual(model.num_parameters(only_trainable=__lowercase		) ,					1_44_10		)

						UpperCAmelCase_								=     AutoModelWithLMHead.from_pretrained(__lowercase ,					from_tf=__lowercase		)
						self.assertIsInstance(__lowercase ,					__lowercase		)
						self.assertEqual(model.num_parameters() ,					1_44_10		)
						self.assertEqual(model.num_parameters(only_trainable=__lowercase		) ,					1_44_10		)


