markdown stringlengths 0 1.02M | code stringlengths 0 832k | output stringlengths 0 1.02M | license stringlengths 3 36 | path stringlengths 6 265 | repo_name stringlengths 6 127 |
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Another Try (Full tree leaning towards 5) | indices = np.where(list(map(lambda x: x>=3,Y_train)))[0]
X_train_counts_3_45 = X_train_counts[indices]
Y_train_3_45 = [0 if Y_train[j]==3 else 1 for j in indices]
indices = np.where(list(map(lambda x:x>3,Y_train)))[0]
X_train_counts_4_5 = X_train_counts[indices]
Y_train_4_5 = [Y_train[j] for j in indices]
indices = n... | _____no_output_____ | MIT | A1_part_1/Non Pipelined Tester.ipynb | ankurshaswat/COL772 |
Another Try | indices = np.where(list(map(lambda x: x>=3,Y_train)))[0]
X_train_counts_3_45 = X_train_counts[indices]
Y_train_3_45 = [0 if Y_train[j]==3 else 1 for j in indices]
indices = np.where(list(map(lambda x:x>3,Y_train)))[0]
X_train_counts_4_5 = X_train_counts[indices]
Y_train_4_5 = [Y_train[j] for j in indices]
indices = n... | _____no_output_____ | MIT | A1_part_1/Non Pipelined Tester.ipynb | ankurshaswat/COL772 |
Another Try | indices = np.where(list(map(lambda x: x<=3,Y_train)))[0]
X_train_counts_12_3 = X_train_counts[indices]
Y_train_12_3 = [1 if Y_train[j]==3 else 0 for j in indices]
indices = np.where(list(map(lambda x:x<3,Y_train)))[0]
X_train_counts_1_2 = X_train_counts[indices]
Y_train_1_2 = [Y_train[j] for j in indices]
indices = n... | _____no_output_____ | MIT | A1_part_1/Non Pipelined Tester.ipynb | ankurshaswat/COL772 |
Tipología de datos · Práctica 22021-6 · Máster universitario en Ciencia de datos (Data Science)Estudios de Informática, Multimedia y Telecomunicación Práctica 2: Limpieza y Análisis de DatosEn esta práctica se elabora un caso práctico orientado a aprender a identificar los datos relevantes para un proyecto analít... | import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import missingno as msno
from statsmodels.graphics.gofplots import qqplot #Normalidad
from scipy import stats #Pruebas Estadísticas
from sklearn.preprocessing import OneHotEncoder
from sklearn import preprocessing
from sklearn.... | _____no_output_____ | MIT | src/Practica2_LimpiezaYAnalisisDatos_Titanic.ipynb | luispicouoc/LimpiezayAnalisisDatos |
1. Descripción del Dataset DatasetTitanic AutorKaggle. Titanic - Machine Learning from Disaster. https://www.kaggle.com/c/titanic/overview DescripciónEste dataset contiene dos conjuntos de datos similares que incluyen información del pasajero como nombre, edad, género, clase, etc. Un conjunto de datos se titula `trai... | titanic_train_original=pd.read_csv('train.csv')
titanic_test_original=pd.read_csv('test.csv')
titanic_train=titanic_train_original.copy()
titanic_test=titanic_test_original.copy()
titanic_train.head()
titanic_test.head() | _____no_output_____ | MIT | src/Practica2_LimpiezaYAnalisisDatos_Titanic.ipynb | luispicouoc/LimpiezayAnalisisDatos |
Procedemos a revisar la estructura y tipos de datos que contiene de los conjuntos de datos, además de los valores únicos de cada atributo. | print('train \n')
titanic_train.info()
print('\n')
print('test \n')
titanic_test.info()
pd.DataFrame(titanic_train.nunique(),columns=['Valores Únicos'])
pd.DataFrame(titanic_test.nunique(),columns=['Valores Únicos']) | _____no_output_____ | MIT | src/Practica2_LimpiezaYAnalisisDatos_Titanic.ipynb | luispicouoc/LimpiezayAnalisisDatos |
Los datos del pasajero estan consituidos por 5 atributos de texto y 6 atributos numéricos, se considera como atributos categóricos a `Pclass` y `Survived`, los nombres de los atributos los guardaremos en dos listas que indiquen cuales son cualitativos y cuales son cuantitativos, adicional el conjunto de train contiene ... | titanic_train['Pclass']=titanic_train['Pclass'].astype('category')
titanic_test['Pclass'] =titanic_test['Pclass'].astype('category')
titanic_train['Survived']=titanic_train['Survived'].astype('category')
atributos_cualitativos=['Sex','Cabin','Embarked','Pclass','Survived']
atributos_cuantitativos=['Age','SibSp','Parc... | <class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 9 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Survived 891 non-null category
1 Pclass 891 non-null category
2 Sex 891 non-null object
3 Age 7... | MIT | src/Practica2_LimpiezaYAnalisisDatos_Titanic.ipynb | luispicouoc/LimpiezayAnalisisDatos |
2.2 Análisis estadístico básicoProcedemos a visualizar estadístos básicos para los atributos cuantitativos `(media, mediana, desvianción estándar, mínimo, máximo, cuartiles)` a través de la función `describe` del dataframe. | titanic_train.describe()
titanic_test.describe() | _____no_output_____ | MIT | src/Practica2_LimpiezaYAnalisisDatos_Titanic.ipynb | luispicouoc/LimpiezayAnalisisDatos |
Procedemos a visualizar estadístos básicos para los atributos cuanlitativos `unique(cantidad de valores únicos),top y frecuencia` a través de la función `describe` del dataframe. | titanic_train[atributos_cualitativos].describe()
titanic_test[atributos_cualitativos[:-1]].describe() | _____no_output_____ | MIT | src/Practica2_LimpiezaYAnalisisDatos_Titanic.ipynb | luispicouoc/LimpiezayAnalisisDatos |
2.3 Selección de DatosDado que el objetivo del análisis será generar una modelo predictivo que permita determinar si un pasajero sobrevivió o no en función de sus atributos sociodemográficos y del viaje, se utilizará como la variable objetivo o dependiente la variable `Survived`* **Sobrevivió (1)**. * **No Sobrevivió ... | pd.DataFrame(np.sum(titanic_train[atributos_cuantitativos]==0),columns=['Ceros'])
pd.DataFrame(np.sum(titanic_test[atributos_cuantitativos]==0),columns=['Ceros']) | _____no_output_____ | MIT | src/Practica2_LimpiezaYAnalisisDatos_Titanic.ipynb | luispicouoc/LimpiezayAnalisisDatos |
Las variables `SibSp` (Número de hermanos(as) /cónyuges a bordo) ,`Parch` (Parch. Número de padres/hijos a bordo) y `Fare` (Tarifa) contienen valores cero, en función de sus definiciones el valor de cero es válido tanto para SibsSp y Parch, pero por la baja cantidad de valores cero se puede considerar que la tarifa fue... | fig = plt.figure(figsize=(4,2))
fig.subplots_adjust(hspace=0.4, wspace=0.4)
ax = fig.add_subplot(1, 2, 1)
msno.matrix(titanic_train,ax=ax,sparkline=False)
ax = fig.add_subplot(1, 2, 2)
msno.bar(titanic_train)
plt.show()
fig = plt.figure(figsize=(4,2))
fig.subplots_adjust(hspace=0.4, wspace=0.4)
ax = fig.add_subplot(1, ... | _____no_output_____ | MIT | src/Practica2_LimpiezaYAnalisisDatos_Titanic.ipynb | luispicouoc/LimpiezayAnalisisDatos |
Las variables `Age` (Edad), `Cabin` (Número de cabina), `Fare` (Tarifa) y `Embarked` contienen valores nulos, para los atributos `Age`, `Fare` y `Embarked` realizaremos un proceso de imputación, en cambio la variable `Cabin` será excluida del análisis por cuanto el **77.81%** (687/891) de los pasajeros no tienen un val... | titanic_train=titanic_train.drop(columns=['Cabin']).copy()
titanic_test=titanic_test.drop(columns=['Cabin']).copy() | _____no_output_____ | MIT | src/Practica2_LimpiezaYAnalisisDatos_Titanic.ipynb | luispicouoc/LimpiezayAnalisisDatos |
**Imputación Atributo Age**El atributo `Age` lo imputaremos con el valor de la media de los pasajeros del conjunto de datos de train. | missing_age = titanic_train[titanic_train['Age'].isna()]
complete_age = titanic_train[~titanic_train['Age'].isna()]
print('Missing')
print(missing_age.describe())
print('Complete')
print(complete_age.describe())
media_age=titanic_train['Age'].mean()
titanic_train = titanic_train.fillna({'Age': media_age})
titanic_test ... | Missing
Age SibSp Parch Fare
count 0.0 177.000000 177.000000 177.000000
mean NaN 0.564972 0.180791 22.158567
std NaN 1.626316 0.534145 31.874608
min NaN 0.000000 0.000000 0.000000
25% NaN 0.000000 0.000000 7.750000
50% NaN 0.000000 0.00... | MIT | src/Practica2_LimpiezaYAnalisisDatos_Titanic.ipynb | luispicouoc/LimpiezayAnalisisDatos |
**Imputación Atributo Fare**El atributo `Fare` lo imputaremos con el valor de la media de los pasajeros del conjunto de datos de train. | missing_fare = titanic_test[titanic_test['Fare'].isna()]
complete_fare = titanic_test[~titanic_test['Fare'].isna()]
print('Missing')
print(missing_fare.describe())
print('Complete')
print(complete_fare.describe())
media_fare=titanic_train['Fare'].mean()
titanic_test = titanic_test.fillna({'Fare': media_fare}) | Missing
Age SibSp Parch Fare
count 1.0 1.0 1.0 0.0
mean 60.5 0.0 0.0 NaN
std NaN NaN NaN NaN
min 60.5 0.0 0.0 NaN
25% 60.5 0.0 0.0 NaN
50% 60.5 0.0 0.0 NaN
75% 60.5 0.0 0.0 NaN
max 60.5 0.0 0.0 NaN
Complete
A... | MIT | src/Practica2_LimpiezaYAnalisisDatos_Titanic.ipynb | luispicouoc/LimpiezayAnalisisDatos |
**Imputación Atributo Embarked**El atributo `Embarked` lo imputaremos con el valor de la moda de los pasajeros del conjunto de datos de train. | missing_embarked = titanic_train[titanic_train['Embarked'].isna()]
complete_embarked = titanic_train[~titanic_train['Embarked'].isna()]
print('Missing')
print(missing_embarked.describe())
print('Complete')
print(complete_embarked.describe())
moda_embarked='S'
titanic_train = titanic_train.fillna({'Embarked': moda_embar... | _____no_output_____ | MIT | src/Practica2_LimpiezaYAnalisisDatos_Titanic.ipynb | luispicouoc/LimpiezayAnalisisDatos |
3.2 Valores ExtremosProcedemos a identificar y dar tratamiento en la medidad de lo posible a los valores extremos que se identifiquen en el conjunto de datos, para esto se utilizará el diagrama de cajas para cada una de los atributos del dataset. | fig = plt.figure(figsize=(15,5))
fig.subplots_adjust(hspace=0.4, wspace=0.4)
for i,atributo in enumerate(titanic_train[atributos_cuantitativos]):
ax = fig.add_subplot(1, 4, i+1)
sns.boxplot(data=titanic_train,y=atributo,ax=ax)
plt.show() | _____no_output_____ | MIT | src/Practica2_LimpiezaYAnalisisDatos_Titanic.ipynb | luispicouoc/LimpiezayAnalisisDatos |
En función de lo observado se identifica que los datos de todas las variables se encuentran en una escala de valores adecuado, por lo que no se sugiere realizar un tratamiento de valores extremos. | titanic_train.to_csv('titanic_train_clean.csv')
titanic_test.to_csv('titanic_test_clean.csv') | _____no_output_____ | MIT | src/Practica2_LimpiezaYAnalisisDatos_Titanic.ipynb | luispicouoc/LimpiezayAnalisisDatos |
4. Análisis de los DatosDado que nuestro objetivo de análisis será generar un modelo que permita clasificar las personas que sobrevieron o no en función de sus atributos y con esto determinar si existían grupos de personas que tenían más probabilidades de sobrevivir que otros, procederemos a seleccionar los grupos de ... | rangos = [0,16,40,60,np.inf]
categorias = ['0-16', '16-40', '40-60', '+60']
titanic_train['Age_range'] = pd.cut(titanic_train['Age'], bins=ranges,labels=group_names)
titanic_test['Age_range'] = pd.cut(titanic_test['Age'], bins=ranges,labels=group_names)
sobrevivio_si=titanic_train[titanic_train['Survived']==1].copy()
... | _____no_output_____ | MIT | src/Practica2_LimpiezaYAnalisisDatos_Titanic.ipynb | luispicouoc/LimpiezayAnalisisDatos |
**Análisis de Atributos Cualitativos** | atributos_cualitativos=['Age_range','Sex','Embarked','Pclass']
fig = plt.figure(figsize=(15,8))
fig.subplots_adjust(hspace=0.4, wspace=0.4)
i=1
for atributo in atributos_cualitativos:
ax = fig.add_subplot(2,4,i)
sns.countplot(data=titanic_train,x=atributo, ax=ax)
ax = fig.add_subplot(2,4,i+1)
sns.count... | _____no_output_____ | MIT | src/Practica2_LimpiezaYAnalisisDatos_Titanic.ipynb | luispicouoc/LimpiezayAnalisisDatos |
* **Rango de Edad** `range_Age` En función del rango de edad podemos determinar que más del 50% de los pasajeros menores a 16 años sobrevivieron, en contraste con el resto de segmentos de edad, siendo las personas de más de 60 años las que en mayor proporción murieron. * **Sexo** `Sex` En función del sexo podemos... | sns.pairplot(titanic_train,hue='Survived',palette=hue_colors)
plt.show() | _____no_output_____ | MIT | src/Practica2_LimpiezaYAnalisisDatos_Titanic.ipynb | luispicouoc/LimpiezayAnalisisDatos |
* **Edad** `Age` En función de la edad del pasajero se ratifica que a menor edad existieron mayor probabilidad de sobrevivir. * **Número de hermanos/cónyuge a bordo** `Sibsip` En función del número de hermanos/cónyuge a bordo podemos determinar que aquellos que tenían 1 tuvieron más probabilidad de sobrevivir. *... | fig = plt.figure(figsize=(15,4))
fig.subplots_adjust(hspace=0.4, wspace=0.4)
for i,variable in enumerate(atributos_cuantitativos):
ax = fig.add_subplot(1, 4, i+1)
ax.set_title(variable)
qqplot(titanic_train[variable], line='s',ax=ax) # q-q plot
stat, p = stats.shapiro(titanic_train[variable])
ax... | _____no_output_____ | MIT | src/Practica2_LimpiezaYAnalisisDatos_Titanic.ipynb | luispicouoc/LimpiezayAnalisisDatos |
Dado que para todas las variable en el test de `Shapiro-Wilk` se obtiene un *p-valor* **inferior** al nivel designificancia **α = 0.05**, entonces se determina que ninguna variable analizada sigue una distribución normal. 4.2.2 Homogeneidad de la Varianza de los DatosSe realizará el test de homogeneidad de la varianza ... | statistic,pvalue = stats.levene(titanic_train.loc[sobrevivio_si.index,'Age'],titanic_train.loc[sobrevivio_no.index,'Age'], center='median')
print('Age : Statistics=%.2f, p-value=%.2f' % (statistic,pvalue))
statistic,pvalue = stats.levene(titanic_train.loc[sobrevivio_si.index,'Fare'],titanic_train.loc[sobrevivio_no.ind... | Age : Statistics=5.48, p-value=0.02
Fare : Statistics=45.10, p-value=0.00
| MIT | src/Practica2_LimpiezaYAnalisisDatos_Titanic.ipynb | luispicouoc/LimpiezayAnalisisDatos |
En función de los test realizados determinamos que los atributos `Age` y `Fare` no tienen homogeneidad de la varianza en sus datos con relación a si el pasajero sobrevivió o no, por cuanto su estadístico *p-valor* es **inferior** al nivel de significancia **α <= 0.05**. 4.3 Pruebas EstadisticasEn función del objetivo d... | statistic,pvalue = stats.kruskal(titanic_train.loc[sobrevivio_si.index,'Age'],titanic_train.loc[sobrevivio_no.index,'Age'], equal_var =False)
print('Age : Statistics=%.2f, p-value=%.2f' % (statistic,pvalue))
statistic,pvalue = stats.kruskal(titanic_train.loc[sobrevivio_si.index,'Fare'],titanic_train.loc[sobrevivio_no.... | Age : Statistics=1.36, p-value=0.24
Fare : Statistics=93.28, p-value=0.00
| MIT | src/Practica2_LimpiezaYAnalisisDatos_Titanic.ipynb | luispicouoc/LimpiezayAnalisisDatos |
En función del test realizado determinamos que el atributo `Age` tiene medianas iguales entre los dos grupos de análisis (sobrevivio_si, sobrevivio_no) por cuanto su estadístico *p-valor* es **superior** al nivel de significancia **α >= 0.05**, no así el atributo `Fare`. 4.3.2 Correlación de Variables | fig = plt.figure(figsize=(8,5))
sns.heatmap(titanic_train.corr(),cmap='Blues',annot=True,cbar=False)
plt.title('Matriz de Correlación')
plt.show() | _____no_output_____ | MIT | src/Practica2_LimpiezaYAnalisisDatos_Titanic.ipynb | luispicouoc/LimpiezayAnalisisDatos |
Como se puede visualizar no existe alguna correlación fuerte entre las variables analizadas. 4.4 Regresión LogísticaProcederemos a realizar el modelo predictivo a través de una regresión logística, para esto realizaremos el proceso para codificar los atributos cuanlitativos a datos numéricos a través de `OneHotEncode... | encoder = OneHotEncoder(drop='first')
codificacion=encoder.fit_transform(titanic_train[['Pclass','Sex','Embarked']]).toarray()
titanic_train_encoding = pd.DataFrame(codificacion,columns=np.hstack(['2','3','male','Q','S']))# encoder.categories_
titanic_train=titanic_train.join(titanic_train_encoding)
titanic_train.drop(... | _____no_output_____ | MIT | src/Practica2_LimpiezaYAnalisisDatos_Titanic.ipynb | luispicouoc/LimpiezayAnalisisDatos |
Para determinar seleccionar los atributos que mayor aportación generen al modelo utilizaremos el proceso de eliminación de atributos recursivo `RFE (feature_selection)`. | logisticRegression = LogisticRegression()
recursiveFeatureElimination = RFE(logisticRegression)
recursiveFeatureElimination = recursiveFeatureElimination.fit(titanic_train, survived.values.ravel())
print(recursiveFeatureElimination.support_)
print(recursiveFeatureElimination.ranking_)
titanic_train=titanic_train.loc[:,... | _____no_output_____ | MIT | src/Practica2_LimpiezaYAnalisisDatos_Titanic.ipynb | luispicouoc/LimpiezayAnalisisDatos |
Como resultado de la selección de atributos, se seleccionaron: `Age` , `Pclass` y `Sex`.**Coeficientes y odds** Utilizaremos el modelo Logit para determinar los coeficientes del modelo. | logit_model=sm.Logit(survived,titanic_train)
resultado=logit_model.fit()
print(resultado.summary2())
print('Odds Ratios')
print(np.exp(resultado.params)) | Optimization terminated successfully.
Current function value: 0.483180
Iterations 6
Results: Logit
=================================================================
Model: Logit Pseudo R-squared: 0.274
Dependent Variable: Survived AIC: ... | MIT | src/Practica2_LimpiezaYAnalisisDatos_Titanic.ipynb | luispicouoc/LimpiezayAnalisisDatos |
**Interpretación de Odds Ratio**En función de los odds ratio del modelo de regresión logística, se puede concluir que:* **Age**. Por cada año de incremento en la edad, la probabilidad de sobrevivir es 0.66 veces menor.* **Pclass (2)**. La probabilidad de sobrevivir es 0.62 veces menor para la pasajeros de segunda clase... | X_train, X_test, y_train, y_test = train_test_split(titanic_train,survived,test_size=0.3,stratify=survived,random_state=24)
logisticRegression = LogisticRegression()
logisticRegression.fit(X_train, y_train)
print('Accuracy of logistic regression classifier on train set: {:.2f} %'.format(logisticRegression.score(X_trai... | Accuracy of logistic regression classifier on train set: 79.61 %
| MIT | src/Practica2_LimpiezaYAnalisisDatos_Titanic.ipynb | luispicouoc/LimpiezayAnalisisDatos |
Como resultado del entrenamiento del modelo se obtuvo un accuracy en el conjunto de entrenamiento de **79.61%** 5. ResultadosCon el modelo entrenado determinamos:* El accuracy en el conjunto de test.* La matriz de confusión de los resultados del modelo. | print('Accuracy of logistic regression classifier on test set: {:.2f}%'.format(logisticRegression.score(X_test, y_test)*100))
matriz_confusion = confusion_matrix(y_test, logisticRegression.predict(X_test))
print('Matriz de Confusión:')
sns.heatmap(matriz_confusion,annot=True,fmt="d",cbar=False,cmap="Blues")
plt.xlabel(... | Accuracy of logistic regression classifier on test set: 76.12%
Matriz de Confusión:
| MIT | src/Practica2_LimpiezaYAnalisisDatos_Titanic.ipynb | luispicouoc/LimpiezayAnalisisDatos |
Como el conjunto de test se obtuvo un accuracy de **76.12%**, finalmente procedemos a calcular si sobrevivieron o no los pasajeros del conjunto `titanic_test`. | titanic_test_original['Survived_Prediction']=logisticRegression.predict(titanic_test)
titanic_test_original | _____no_output_____ | MIT | src/Practica2_LimpiezaYAnalisisDatos_Titanic.ipynb | luispicouoc/LimpiezayAnalisisDatos |
6. ConclusionesEn función de los resultados, podemos concluir que se obtiene un modelo relativamente bueno para clasificar si un pasajero sobrevivió o no, la precisión global del modelo es del **76.12%**. Esta precisión podría mejorarse utilizando modelos más avanzados bsados en árboles o redes neuronales.Además hemos... | pd.DataFrame({'CONTRIBUCIONES':['Investigación Previa','Redacción de las Respuestas','Desarrollo código'],
'FIRMA':['LP','LP','LP']}) | _____no_output_____ | MIT | src/Practica2_LimpiezaYAnalisisDatos_Titanic.ipynb | luispicouoc/LimpiezayAnalisisDatos |
Flat Map Chains DemoExample demonstrateing how to extract protein chains from PDB entries. This example uses a flatMap function to transform a structure to its polymer chains. Imports | from pyspark.sql import SparkSession
from mmtfPyspark.filters import PolymerComposition
from mmtfPyspark.io import mmtfReader
from mmtfPyspark.mappers import StructureToPolymerChains | _____no_output_____ | Apache-2.0 | demos/mappers/FlatMapChainsDemo.ipynb | sbliven/mmtf-pyspark |
Configure Spark | spark = SparkSession.builder.appName("FlatMapChainsDemo").getOrCreate() | _____no_output_____ | Apache-2.0 | demos/mappers/FlatMapChainsDemo.ipynb | sbliven/mmtf-pyspark |
Read in MMTF files | path = "../../resources/mmtf_reduced_sample/"
pdb = mmtfReader.read_sequence_file(path) | _____no_output_____ | Apache-2.0 | demos/mappers/FlatMapChainsDemo.ipynb | sbliven/mmtf-pyspark |
flat map structure to polymer chains, filter by polymer composition and count Supported polymer composition type:** polymerComposition.AMINO_ACIDS_20 **= ["ALA","ARG","ASN","ASP","CYS","GLN","GLU","GLY","HIS","ILE","LEU","LYS","MET","PHE","PRO","SER","THR","TRP","TYR","VAL"]** polymerComposition.AMINO_ACIDS_22 **= ["... | count = pdb.flatMap(StructureToPolymerChains(False, True)) \
.filter(PolymerComposition(PolymerComposition.AMINO_ACIDS_20)) \
.count()
print(f"Chains with standard amino acids: {count}") | Chains with standard amino acids: 8346
| Apache-2.0 | demos/mappers/FlatMapChainsDemo.ipynb | sbliven/mmtf-pyspark |
Terminate Spark | spark.stop() | _____no_output_____ | Apache-2.0 | demos/mappers/FlatMapChainsDemo.ipynb | sbliven/mmtf-pyspark |
Kubeflow pipelinesThis notebook goes through the steps of using Kubeflow pipelines using the Python3 interpreter (command-line) to preprocess, train, tune and deploy the babyweight model. 1. Start Hosted Pipelines and NotebookTo try out this notebook, first launch Kubeflow Hosted Pipelines and an AI Platform Notebook... | %pip install --quiet kfp python-dateutil --upgrade | _____no_output_____ | Apache-2.0 | courses/machine_learning/deepdive/06_structured/7_pipelines.ipynb | ranshiloni/training-data-analyst |
Make sure to *restart the kernel* to pick up new packages (look for button in the ribbon of icons above this notebook) 3. Connect to the Hosted PipelinesVisit https://console.cloud.google.com/ai-platform/pipelines/clustersand get the hostname for your cluster. You can get it by clicking on the Settings icon.Alternate... | # CHANGE THESE
PIPELINES_HOST='447cdd24f70c9541-dot-us-central1.notebooks.googleusercontent.com'
PROJECT='ai-analytics-solutions'
BUCKET='ai-analytics-solutions-kfpdemo'
import kfp
client = kfp.Client(host=PIPELINES_HOST)
#client.list_pipelines() | _____no_output_____ | Apache-2.0 | courses/machine_learning/deepdive/06_structured/7_pipelines.ipynb | ranshiloni/training-data-analyst |
4. [Optional] Build Docker containersI have made my containers public, so you can simply use those. | %%bash
cd pipelines/containers
#bash build_all.sh | _____no_output_____ | Apache-2.0 | courses/machine_learning/deepdive/06_structured/7_pipelines.ipynb | ranshiloni/training-data-analyst |
Check that the Docker images work properly ... | #!docker pull gcr.io/cloud-training-demos/babyweight-pipeline-bqtocsv:latest
#!docker run -t gcr.io/cloud-training-demos/babyweight-pipeline-bqtocsv:latest --project $PROJECT --bucket $BUCKET --local | _____no_output_____ | Apache-2.0 | courses/machine_learning/deepdive/06_structured/7_pipelines.ipynb | ranshiloni/training-data-analyst |
5. Upload and execute pipelineUpload to the Kubeflow pipeline cluster | from pipelines import mlp_babyweight
pipeline = client.create_run_from_pipeline_func(mlp_babyweight.train_and_deploy,
arguments={'project': PROJECT, 'bucket': BUCKET})
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you ... | _____no_output_____ | Apache-2.0 | courses/machine_learning/deepdive/06_structured/7_pipelines.ipynb | ranshiloni/training-data-analyst |
Analyze Worldwide Box Office Revenue with Plotly and Python Libraries | import numpy as np
import pandas as pd
pd.set_option('max_columns', None)
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
plt.style.use('ggplot')
import datetime
import lightgbm as lgb
from scipy import stats
from scipy.sparse import hstack, csr_matrix
from sklearn.model_selection import train_... | /usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.
import pandas.util.testing as tm
| MIT | BOX_OFFICE_ANALYSIS_WITH_PLOTLY_AND_PY.ipynb | itsdharmil/box-office-analysis-with-plotly-and-python |
Data Loading and Exploration | train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')
train.head() | _____no_output_____ | MIT | BOX_OFFICE_ANALYSIS_WITH_PLOTLY_AND_PY.ipynb | itsdharmil/box-office-analysis-with-plotly-and-python |
Visualizing the Target Distribution | fig, ax = plt.subplots(figsize = (16, 6))
plt.subplot(1, 2, 1)
plt.hist(train['revenue']);
plt.title('Distribution of revenue');
plt.subplot(1, 2, 2)
plt.hist(np.log1p(train['revenue']));
plt.title('Distribution of log of revenue');
train['log_revenue'] = np.log1p(train['revenue']) | _____no_output_____ | MIT | BOX_OFFICE_ANALYSIS_WITH_PLOTLY_AND_PY.ipynb | itsdharmil/box-office-analysis-with-plotly-and-python |
Relationship between Film Revenue and Budget | fig, ax = plt.subplots(figsize = (16, 6))
plt.subplot(1, 2, 1)
plt.hist(train['budget']);
plt.title('Distribution of budget');
plt.subplot(1, 2, 2)
plt.hist(np.log1p(train['budget']));
plt.title('Distribution of log of budget');
plt.figure(figsize=(16, 8))
plt.subplot(1, 2, 1)
plt.scatter(train['budget'], train['revenu... | _____no_output_____ | MIT | BOX_OFFICE_ANALYSIS_WITH_PLOTLY_AND_PY.ipynb | itsdharmil/box-office-analysis-with-plotly-and-python |
Does having an Official Homepage Affect Revenue? | train['homepage'].value_counts().head(10)
train['has_homepage'] = 0
train.loc[train['homepage'].isnull() == False, 'has_homepage'] = 1
test['has_homepage'] = 0
test.loc[test['homepage'].isnull() == False, 'has_homepage'] = 1
sns.catplot(x='has_homepage', y='revenue', data=train);
plt.title('Revenue for film with and wi... | _____no_output_____ | MIT | BOX_OFFICE_ANALYSIS_WITH_PLOTLY_AND_PY.ipynb | itsdharmil/box-office-analysis-with-plotly-and-python |
Distribution of Languages in Film | plt.figure(figsize=(16, 8))
plt.subplot(1, 2, 1)
sns.boxplot(x='original_language', y='revenue', data=train.loc[train['original_language'].isin(train['original_language'].value_counts().head(10).index)]);
plt.title('Mean revenue per language');
plt.subplot(1, 2, 2)
sns.boxplot(x='original_language', y='log_revenue', da... | _____no_output_____ | MIT | BOX_OFFICE_ANALYSIS_WITH_PLOTLY_AND_PY.ipynb | itsdharmil/box-office-analysis-with-plotly-and-python |
Frequent Words in Film Titles and Discriptions | plt.figure(figsize = (12, 12))
text = ' '.join(train['original_title'].values)
wordcloud = WordCloud(max_font_size=None, background_color='white', width=1200, height=1000).generate(text)
plt.imshow(wordcloud)
plt.title('Top words in titles')
plt.axis("off")
plt.show()
plt.figure(figsize = (12, 12))
text = ' '.join(trai... | _____no_output_____ | MIT | BOX_OFFICE_ANALYSIS_WITH_PLOTLY_AND_PY.ipynb | itsdharmil/box-office-analysis-with-plotly-and-python |
Do Film Descriptions Impact Revenue? | import eli5
vectorizer = TfidfVectorizer(
sublinear_tf=True,
analyzer='word',
token_pattern=r'\w{1,}',
ngram_range=(1, 2),
min_df=5)
overview_text = vectorizer.fit_transform(train['overview'].fillna(''))
linreg = LinearRegression()
linreg.fit(overview_text, ... | Target value: 16.44583954907521
| MIT | BOX_OFFICE_ANALYSIS_WITH_PLOTLY_AND_PY.ipynb | itsdharmil/box-office-analysis-with-plotly-and-python |
Analyzing Movie Release Dates | test.loc[test['release_date'].isnull()==False,'release_date'].head() | _____no_output_____ | MIT | BOX_OFFICE_ANALYSIS_WITH_PLOTLY_AND_PY.ipynb | itsdharmil/box-office-analysis-with-plotly-and-python |
Preprocessing Features | def fix_date(x):
year = x.split('/')[2]
if int(year)<=19:
return x[:-2] + '20' + year
else:
return x[:-2] + '19' + year
test.loc[test['release_date'].isnull() == True].head()
test.loc[test['release_date'].isnull() == True, 'release_date'] = '05/01/00'
train['release_date'] = train['rele... | _____no_output_____ | MIT | BOX_OFFICE_ANALYSIS_WITH_PLOTLY_AND_PY.ipynb | itsdharmil/box-office-analysis-with-plotly-and-python |
Creating Features Based on Release Date | train['release_date'] = pd.to_datetime(train['release_date'])
test['release_date'] = pd.to_datetime(test['release_date'])
def process_date(df):
date_parts = ['year','weekday','month','weekofyear','day','quarter']
for part in date_parts:
part_col = 'release_date' + '_' + part
df[part_col] = geta... | _____no_output_____ | MIT | BOX_OFFICE_ANALYSIS_WITH_PLOTLY_AND_PY.ipynb | itsdharmil/box-office-analysis-with-plotly-and-python |
Using Plotly to Visualize the Number of Films Per Year | d1=train['release_date_year'].value_counts().sort_index()
d2=test['release_date_year'].value_counts().sort_index()
import plotly.offline as py
py.init_notebook_mode(connected=True)
import plotly.graph_objs as go
data = [go.Scatter(x=d1.index, y =d1.values, name='train'),
go.Scatter(x=d2.index,y=d2.values,name='... | _____no_output_____ | MIT | BOX_OFFICE_ANALYSIS_WITH_PLOTLY_AND_PY.ipynb | itsdharmil/box-office-analysis-with-plotly-and-python |
Number of Films and Revenue Per Year | #d1 = train['release_date_year'].value.counts().sort_index()
d1=train['release_date_year'].value_counts().sort_index()
d2 = train.groupby(['release_date_year'])['revenue'].sum()
data = [go.Scatter (x=d1.index,y=d1.values,name='film count'),
go.Scatter(x=d2.index, y=d2.values,name='total revenue',yaxis='y2')]
l... | _____no_output_____ | MIT | BOX_OFFICE_ANALYSIS_WITH_PLOTLY_AND_PY.ipynb | itsdharmil/box-office-analysis-with-plotly-and-python |
Do Release Days Impact Revenue? | sns.catplot(x='release_date_weekday', y='revenue',data=train);
plt.title('revenue of dif days of the week') | _____no_output_____ | MIT | BOX_OFFICE_ANALYSIS_WITH_PLOTLY_AND_PY.ipynb | itsdharmil/box-office-analysis-with-plotly-and-python |
Relationship between Runtime and Revenue | sns.distplot(train['runtime'].fillna(0)/60,bins=(40),kde=False);
plt.title('distribution of flims in hrs')
sns.scatterplot(train['runtime'].fillna(0)/60, train['revenue'])
plt.title('runtime vs revenue') | _____no_output_____ | MIT | BOX_OFFICE_ANALYSIS_WITH_PLOTLY_AND_PY.ipynb | itsdharmil/box-office-analysis-with-plotly-and-python |
Large File Input Source: [https://github.com/d-insight/code-bank.git](https://github.com/d-insight/code-bank.git) License: [MIT License](https://opensource.org/licenses/MIT). See open source [license](LICENSE) in the Code Bank repository. --- Import libraries | import numpy as np
import pandas as pd
from pathlib import Path
import csv
from pprint import pprint
import sqlite3
from sqlalchemy import create_engine | _____no_output_____ | MIT | _development/tutorials/automate-excel/4-large-file.ipynb | dsfm-org/code-bank |
Set data directory | DIR = 'data'
FILE = '/yellow_tripdata_2016-03.csv'
csv_file = '{}{}'.format(DIR, FILE)
print('File directory: {}'.format(csv_file)) | File directory: data/yellow_tripdata_2016-03.csv
| MIT | _development/tutorials/automate-excel/4-large-file.ipynb | dsfm-org/code-bank |
How large is the dataset on disk? | size = Path(csv_file).stat().st_size
f'{size:,} bytes' | _____no_output_____ | MIT | _development/tutorials/automate-excel/4-large-file.ipynb | dsfm-org/code-bank |
How many rows of data? | with open(csv_file) as f:
row_count = sum(1 for row in f) # generator expression
f'{row_count:,} rows' | _____no_output_____ | MIT | _development/tutorials/automate-excel/4-large-file.ipynb | dsfm-org/code-bank |
Inspect Raw Data | with open(csv_file) as f:
for i, row in enumerate(f):
pprint(row)
if i == 3:
break | 'VendorID,tpep_pickup_datetime,tpep_dropoff_datetime,passenger_count,trip_distance,pickup_longitude,pickup_latitude,RatecodeID,store_and_fwd_flag,dropoff_longitude,dropoff_latitude,payment_type,fare_amount,extra,mta_tax,tip_amount,tolls_amount,improvement_surcharge,total_amount\n'
('1,2016-03-01 00:00:00,2016-03-01 '
... | MIT | _development/tutorials/automate-excel/4-large-file.ipynb | dsfm-org/code-bank |
Cleaner view | with open(csv_file) as f:
csv_dict = csv.DictReader(f)
for i, row in enumerate(csv_dict):
pprint(row)
if i ==3:
break | {'RatecodeID': '1',
'VendorID': '1',
'dropoff_latitude': '40.746128082275391',
'dropoff_longitude': '-74.004264831542969',
'extra': '0.5',
'fare_amount': '9',
'improvement_surcharge': '0.3',
'mta_tax': '0.5',
'passenger_count': '1',
'payment_type': '1',
'pickup_latitude': '40.765151977539062',
'pickup_longit... | MIT | _development/tutorials/automate-excel/4-large-file.ipynb | dsfm-org/code-bank |
Import few rows using pandas | print(pd.read_csv(csv_file, nrows=2)) | VendorID tpep_pickup_datetime tpep_dropoff_datetime passenger_count \
0 1 2016-03-01 00:00:00 2016-03-01 00:07:55 1
1 1 2016-03-01 00:00:00 2016-03-01 00:11:06 1
trip_distance pickup_longitude pickup_latitude RatecodeID \
0 2.5 -73... | MIT | _development/tutorials/automate-excel/4-large-file.ipynb | dsfm-org/code-bank |
Loaded succesfully! Calculating Size Let’s load the first 10,000 lines into pandas and measure the amount of memory being used. Then we can calculate the total amount of memory needed to load the complete file | nrows = 10_000
MB = 2**20 # 1 MB = 2**20 bytes = 1,048,576 bytes
df = pd.read_csv(csv_file, nrows=nrows)
df_mb = df.memory_usage(deep=True).sum() / MB
df_mb # how much memory the DataFrame is consuming in MB
df_mb * (row_count / nrows) # total memory consumption for the whole data file. ~4GB | _____no_output_____ | MIT | _development/tutorials/automate-excel/4-large-file.ipynb | dsfm-org/code-bank |
Import Standard way -> Memory Error! | #df = pd.read_csv(file)
df.shape | _____no_output_____ | MIT | _development/tutorials/automate-excel/4-large-file.ipynb | dsfm-org/code-bank |
#df.info() | _____no_output_____ | MIT | _development/tutorials/automate-excel/4-large-file.ipynb | dsfm-org/code-bank | |
Solution A: Chunking! | chunk_size=50000
batch_no=1
for chunk in pd.read_csv(csv_file,chunksize=chunk_size):
chunk.to_csv(DIR+'chunk'+str(batch_no)+'.csv',index=False)
batch_no+=1 | _____no_output_____ | MIT | _development/tutorials/automate-excel/4-large-file.ipynb | dsfm-org/code-bank |
Importing a single chunk file into pandas dataframe: | DIR
df1 = pd.read_csv(DIR+'chunk2.csv')
df1.head() | _____no_output_____ | MIT | _development/tutorials/automate-excel/4-large-file.ipynb | dsfm-org/code-bank |
Solution B: SQL!1. Create a Connector to a Database2. Build the Database (load CSV File) by Chunking3. Construct Pandas DF from Database using SQL query | # Create a Connector to a Database (creates it in Memory - not an SQL db)
csv_database = create_engine('sqlite:///csv_database.db')
# Build the Database (load CSV File) by Chunking
chunk_size=10000
i=0
j=0
for df in pd.read_csv(csv_file, chunksize=chunk_size, iterator=True):
df = df.rename(columns= {c: c.replace('... | _____no_output_____ | MIT | _development/tutorials/automate-excel/4-large-file.ipynb | dsfm-org/code-bank |
Decoding sensor space data with generalization across time and conditionsThis example runs the analysis described in [1]_. It illustrates how one canfit a linear classifier to identify a discriminatory topography at a given timeinstant and subsequently assess whether this linear model can accuratelypredict all of the ... | # Authors: Jean-Remi King <jeanremi.king@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import Stan... | _____no_output_____ | BSD-3-Clause | 0.16/_downloads/plot_decoding_time_generalization_conditions.ipynb | drammock/mne-tools.github.io |
Copyright 2019 The TensorFlow Authors. | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... | _____no_output_____ | Apache-2.0 | site/el/guide/keras/overview.ipynb | KarimaTouati/docs-l10n |
Σύνοψη Keras Άνοιγμα στο TensorFlow.org Εκτέλεση στο Google Colab Προβολή πηγαίου στο GitHub Λήψη "σημειωματάριου" Note: Η κοινότητα του TensorFlow έχει μεταφράσει αυτά τα έγγραφα. Καθότι οι μεταφράσεις αυτές αποτελούν την καλύτερη δυνατή προσπάθεια , δεν υπάρχει εγγύηση ότι θα παραμεί... | from __future__ import absolute_import, division, print_function, unicode_literals
try:
# %tensorflow_version only exists in Colab.
%tensorflow_version 2.x
except Exception:
pass
import tensorflow as tf
from tensorflow import keras | _____no_output_____ | Apache-2.0 | site/el/guide/keras/overview.ipynb | KarimaTouati/docs-l10n |
Το tf.keras μπορέι να τρέξει οποιοδήποτε κομμάτι συμβατού κώδικα Keras , ωστόσο να έχετε υπόψιν σας τα εξής : * Η έκδοση `tf.keras` της τελευταίας έκδοσης του TensorFlow μπορεί να μην ταυτίζεται με την τελευταία έκδοση `keras` από το PyPI. Ελέγξτε το `tf.keras.__version__.`* Όταν [αποθηκεύετε τα βάρη ενός μοντέλου](htt... | from tensorflow.keras import layers
model = tf.keras.Sequential()
# Adds a densely-connected layer with 64 units to the model:
model.add(layers.Dense(64, activation='relu'))
# Add another:
model.add(layers.Dense(64, activation='relu'))
# Add a softmax layer with 10 output units:
model.add(layers.Dense(10, activation='... | _____no_output_____ | Apache-2.0 | site/el/guide/keras/overview.ipynb | KarimaTouati/docs-l10n |
Μπορείτε να βρείτε ένα ολοκληρωμένο και σύντομο παράδειγμα για το πώς χρησιμοποιούνται τα σειριακά μοντέλα [εδώ](https://www.tensorflow.org/tutorials/quickstart/beginner)Για να μάθετε να δημιουργείτε πιο προχωρημένα μοντέλα από τα σειριακά , δέιτε τα εξής :* [Οδηγός για το Keras functional API link text](https://colab... | # Create a sigmoid layer:
layers.Dense(64, activation='sigmoid')
# Or:
layers.Dense(64, activation=tf.keras.activations.sigmoid)
# A linear layer with L1 regularization of factor 0.01 applied to the kernel matrix:
layers.Dense(64, kernel_regularizer=tf.keras.regularizers.l1(0.01))
# A linear layer with L2 regularizat... | _____no_output_____ | Apache-2.0 | site/el/guide/keras/overview.ipynb | KarimaTouati/docs-l10n |
Μάθηση και αξιολόγηση Στήνοντας την μάθησηΑφότου έχει κατασκευαστεί το μοντέλο, ορίστε την διαδικασία μάθησης καλώντας την μέθοδο `compile`: | model = tf.keras.Sequential([
# Adds a densely-connected layer with 64 units to the model:
layers.Dense(64, activation='relu', input_shape=(32,)),
# Add another:
layers.Dense(64, activation='relu'),
# Add a softmax layer with 10 output units:
layers.Dense(10, activation='softmax')])
model.compile(optimizer=tf.keras.op... | _____no_output_____ | Apache-2.0 | site/el/guide/keras/overview.ipynb | KarimaTouati/docs-l10n |
Η μέθοδος `tf.keras.Model.compile` δέχεται 3 σημαντικά ορίσματα : * `optimizer` : Το αντικείμενο αυτό προσδιορίζει την διαδικασία μάθησης. Περάστε στιγμιότυπα βελτιστοποίησης (optimizer instances) από το άρθρωμα (module) `tf.keras.optimizers` , όπως `tf.keras.optimizers.Adam` ή `tf.keras.optimizers.SGD`. Αν θέλετε να ... | # Configure a model for mean-squared error regression.
model.compile(optimizer=tf.keras.optimizers.Adam(0.01),
loss='mse', # mean squared error
metrics=['mae']) # mean absolute error
# Configure a model for categorical classification.
model.compile(optimizer=tf.keras.optimizers.RMSpr... | _____no_output_____ | Apache-2.0 | site/el/guide/keras/overview.ipynb | KarimaTouati/docs-l10n |
Μάθηση από NumPy δεδομέναΣε μικρό όγκο δεδομένων,συνιστάται η χρήση των in-memory πινάκων [NumPy](https://numpy.org/) για την μάθηση και την αξιολόγηση ενός μοντέλου. Το μοντέλο "ταιριάζει" στα δεδομένα εκμάθησης μέσω της μεθόδου `fit` : | import numpy as np
data = np.random.random((1000, 32))
labels = np.random.random((1000, 10))
model.fit(data, labels, epochs=10, batch_size=32) | _____no_output_____ | Apache-2.0 | site/el/guide/keras/overview.ipynb | KarimaTouati/docs-l10n |
Η μέθοδος `tf.keras.Model.fit` δέχεται 3 ορίσματα : * `epochs` : Η μάθηση οργανώνεται σε *εποχές* (epochs). *Εποχή* είναι μία επανάληψη σε όλο τον όγκο των δεδομένων εισόδου(αυτό γίνεται πρώτα σε μικρότερες "δεσμίδες" δεδομένων)* `batch_size` : Όταν περνάνε τα δεδομένα NumPy , το μοντέλο τα τεμαχίζει σε (μικρότερες) "... | import numpy as np
data = np.random.random((1000, 32))
labels = np.random.random((1000, 10))
val_data = np.random.random((100, 32))
val_labels = np.random.random((100, 10))
model.fit(data, labels, epochs=10, batch_size=32,
validation_data=(val_data, val_labels)) | _____no_output_____ | Apache-2.0 | site/el/guide/keras/overview.ipynb | KarimaTouati/docs-l10n |
Μάθηση από σύνολα δεδομένων(datasets) tf.dataΧρησιμοποιείστε τo [Datasets API](https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/guide/data.ipynb) για να κλιμακώσετε μεγάλα σύνολα δεδομένων ή μάθηση σε πολλές συσκευές. Περάστε ένα `tf.data.Dataset` στιγμιότυπο στην μέθοδο `fit` : | # Instantiates a toy dataset instance:
dataset = tf.data.Dataset.from_tensor_slices((data, labels))
dataset = dataset.batch(32)
model.fit(dataset, epochs=10) | _____no_output_____ | Apache-2.0 | site/el/guide/keras/overview.ipynb | KarimaTouati/docs-l10n |
Από τη στιγμή που το `Dataset` παράγει "δεσμίδες" δεδομένων , αυτό το απόσπασμα δεν απαίτει το `batch_size`. Τα σύνολα δεδομένων μπορούν να χρησιμοποιηθούν και για επιβεβαίωση : | dataset = tf.data.Dataset.from_tensor_slices((data, labels))
dataset = dataset.batch(32)
val_dataset = tf.data.Dataset.from_tensor_slices((val_data, val_labels))
val_dataset = val_dataset.batch(32)
model.fit(dataset, epochs=10,
validation_data=val_dataset) | _____no_output_____ | Apache-2.0 | site/el/guide/keras/overview.ipynb | KarimaTouati/docs-l10n |
Αξιολόγηση και πρόβλεψηΟι `tf.keras.Model.evaluate` και `tf.keras.Model.predict` μέθοδοι μπορούν να χρησιμοποιήσουν δεδομένα NumPy και ένα `tf.data.Dataset`.Ακολουθεί ο τρόπος αξιολόγησης "συμπερασματικής" λειτουργίας των απωλειών και των μετρήσεων για τα παρεχόμενα δεδομένα : | # With Numpy arrays
data = np.random.random((1000, 32))
labels = np.random.random((1000, 10))
model.evaluate(data, labels, batch_size=32)
# With a Dataset
dataset = tf.data.Dataset.from_tensor_slices((data, labels))
dataset = dataset.batch(32)
model.evaluate(dataset) | _____no_output_____ | Apache-2.0 | site/el/guide/keras/overview.ipynb | KarimaTouati/docs-l10n |
Με αυτό το απόσπασμα μπορείτε να προβλέψετε την έξοδο του τελευταίου επιπέδου σε inference για τα παρεχόμενα δεδομένα , ως έναν πίνακα NumPy : | result = model.predict(data, batch_size=32)
print(result.shape) | _____no_output_____ | Apache-2.0 | site/el/guide/keras/overview.ipynb | KarimaTouati/docs-l10n |
Για έναν ολοκληρωμένο οδηγό στην εκμάθηση και την αξιολόγηση , ο οποίος περιλαμβάνει και οδηγίες για συγγραφή προσαρμοσμένων loops μαθήσεως από το μηδέν , δείτε τον οδηγό [αυτό](https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/guide/keras/train_and_evaluate.ipynb). Δημιουργία σύνθετων μοντέ... | inputs = tf.keras.Input(shape=(32,)) # Returns an input placeholder
# A layer instance is callable on a tensor, and returns a tensor.
x = layers.Dense(64, activation='relu')(inputs)
x = layers.Dense(64, activation='relu')(x)
predictions = layers.Dense(10, activation='softmax')(x) | _____no_output_____ | Apache-2.0 | site/el/guide/keras/overview.ipynb | KarimaTouati/docs-l10n |
Δώστε υπόσταση στο μοντέλο με είσοδο και έξοδο: | model = tf.keras.Model(inputs=inputs, outputs=predictions)
# The compile step specifies the training configuration.
model.compile(optimizer=tf.keras.optimizers.RMSprop(0.001),
loss='categorical_crossentropy',
metrics=['accuracy'])
# Trains for 5 epochs
model.fit(data, labels, batch_size=32... | _____no_output_____ | Apache-2.0 | site/el/guide/keras/overview.ipynb | KarimaTouati/docs-l10n |
Υποκατηγοριοποίηση στα μοντέλαΦτιάξτε ένα πλήρως παραμετροποιήσιμο μοντέλο υποκατηγοριοποιώντας την `tf.keras.Model` και ορίζοντας το δικό σας "προς τα εμπρος πέρασμα"(forward pass,κοινώς διαδικασία υπολογισμού η οποία ξεκινάει από το 1ο προς το τελευταίο επίπεδο). Κατασκευάστε επίπεδα στην μέθοδο `__init__` και θέστε... | class MyModel(tf.keras.Model):
def __init__(self, num_classes=10):
super(MyModel, self).__init__(name='my_model')
self.num_classes = num_classes
# Define your layers here.
self.dense_1 = layers.Dense(32, activation='relu')
self.dense_2 = layers.Dense(num_classes, activation='sigmoid')
def call... | _____no_output_____ | Apache-2.0 | site/el/guide/keras/overview.ipynb | KarimaTouati/docs-l10n |
Η νέα κλάση model λαμβάνει υπόσταση : | model = MyModel(num_classes=10)
# The compile step specifies the training configuration.
model.compile(optimizer=tf.keras.optimizers.RMSprop(0.001),
loss='categorical_crossentropy',
metrics=['accuracy'])
# Trains for 5 epochs.
model.fit(data, labels, batch_size=32, epochs=5) | _____no_output_____ | Apache-2.0 | site/el/guide/keras/overview.ipynb | KarimaTouati/docs-l10n |
Παραμετροποιήσιμα επίπεδαΔημιουργήστε ένα παραμετροποιήσιμο επίπεδο υποκατηγοριοποιώντας την `tf.keras.layers.Layer` και υλοποιώντας τις παρακάτω μεθόδους :* `__init__` : (Προαιρετικά) ορίστε τα υποεπίπεδα που θα χρησιμοποιηθούν από το επίπεδο* `build` : Δημιουργεί τα βάρη του επιπέδου. Προσθέστε βάρη με την μέθοδο `a... | class MyLayer(layers.Layer):
def __init__(self, output_dim, **kwargs):
self.output_dim = output_dim
super(MyLayer, self).__init__(**kwargs)
def build(self, input_shape):
# Create a trainable weight variable for this layer.
self.kernel = self.add_weight(name='kernel',
... | _____no_output_____ | Apache-2.0 | site/el/guide/keras/overview.ipynb | KarimaTouati/docs-l10n |
Δημιουργήστε ένα μοντέλο χρησιμοποιώντας το δικό σας επίπεδο : | model = tf.keras.Sequential([
MyLayer(10),
layers.Activation('softmax')])
# The compile step specifies the training configuration
model.compile(optimizer=tf.keras.optimizers.RMSprop(0.001),
loss='categorical_crossentropy',
metrics=['accuracy'])
# Trains for 5 epochs.
model.fit(data... | _____no_output_____ | Apache-2.0 | site/el/guide/keras/overview.ipynb | KarimaTouati/docs-l10n |
ΕπανακλήσειςΜία επανάκληση(callback) είναι ένα αντικείμενο το οποίο περνάει σε ένα μοντέλο για να τροποποιήσει και να επεκτείνει τη συμπεριφορά του κατά την διάρκεια της μάθησης. Μπορείτε να γράψετε τις δικές σας επανακλήσεις ή να χρησιμοποιήσετε την `tf.keras.callbacks` η οποία περιλαμβάνει :* `tf.keras.callbacks.Mode... | callbacks = [
# Interrupt training if `val_loss` stops improving for over 2 epochs
tf.keras.callbacks.EarlyStopping(patience=2, monitor='val_loss'),
# Write TensorBoard logs to `./logs` directory
tf.keras.callbacks.TensorBoard(log_dir='./logs')
]
model.fit(data, labels, batch_size=32, epochs=5, callbacks=callba... | _____no_output_____ | Apache-2.0 | site/el/guide/keras/overview.ipynb | KarimaTouati/docs-l10n |
Αποθήκευση και επαναφορά Αποθήκευση μόνο των τιμών των βαρώνΑποθηκεύστε και φορτώστε τα βάρη ενός μοντέλου με τη χρήση της `tf.keras.Model.save_weights`: | model = tf.keras.Sequential([
layers.Dense(64, activation='relu', input_shape=(32,)),
layers.Dense(10, activation='softmax')])
model.compile(optimizer=tf.keras.optimizers.Adam(0.001),
loss='categorical_crossentropy',
metrics=['accuracy'])
# Save weights to a TensorFlow Checkpoint file
model... | _____no_output_____ | Apache-2.0 | site/el/guide/keras/overview.ipynb | KarimaTouati/docs-l10n |
Ως προεπιλογή , το παραπάνω αποθηκεύει τα βάρη του μοντέλου σε μορφοποίηση [TensorFlow checkpoint](../checkpoint.ipynb). Εναλλακτικά , μπορούν να αποθηκευτούν χρησιμοποιώντας την μορφοποιήση Keras HDF5(η προεπιλογή για την υλοποίηση του συστήματος υποστήριξης του Keras): | # Save weights to a HDF5 file
model.save_weights('my_model.h5', save_format='h5')
# Restore the model's state
model.load_weights('my_model.h5') | _____no_output_____ | Apache-2.0 | site/el/guide/keras/overview.ipynb | KarimaTouati/docs-l10n |
Αποθήκευση μόνο των ρυθμίσεων του μοντέλουΟι ρυθμίσεις ενός μοντέλου μπορούν να αποθηκευτούν-αυτό σειριοποιεί την αρχιτεκτονική του μοντέλου χωρίς βάρη. Οι αποθηκευμένες ρυθμίσεις μπορόυν να αναπαράγουν και να αρχικοποιήσουν το ίδιο μοντέλο, ακόμη και χωρίς τον κώδικα που όρισε το πρότυπο μοντέλο. To Keras υποστηρίζει... | # Serialize a model to JSON format
json_string = model.to_json()
json_string
import json
import pprint
pprint.pprint(json.loads(json_string)) | _____no_output_____ | Apache-2.0 | site/el/guide/keras/overview.ipynb | KarimaTouati/docs-l10n |
Αναπαραγωγή του μοντέλου(νεότερα αρχικοποιήθεντος) από το JSON : | fresh_model = tf.keras.models.model_from_json(json_string) | _____no_output_____ | Apache-2.0 | site/el/guide/keras/overview.ipynb | KarimaTouati/docs-l10n |
Η σειριοποίηση μοντέλου σε μορφή YAML απαιτεί την εγκατάσταση της `pyyaml` πριν γίνει είσοδος της TensorFlow: | yaml_string = model.to_yaml()
print(yaml_string) | _____no_output_____ | Apache-2.0 | site/el/guide/keras/overview.ipynb | KarimaTouati/docs-l10n |
Αναπαραγωγή του μοντέλου από το YAML : | fresh_model = tf.keras.models.model_from_yaml(yaml_string) | _____no_output_____ | Apache-2.0 | site/el/guide/keras/overview.ipynb | KarimaTouati/docs-l10n |
Προσοχή : Μοντέλα ως υποκλάσεις δεν σειριοποιούνται διότι η αρχιτεκτονική τους ορίζεται από τον κώδικα Python στο σώμα τις μεθόδου `call`. Αποθήκευση ολόκληρου του μοντέλου σε ένα αρχείοΟλόκληρο το μοντέλο μπορεί να αποθηκευτεί σε ένα μόνο αρχείο το οποίο περιέχει τα βάρη , τις ρυθμίσεις του μοντέλου , ακόμη και τις ρ... | # Create a simple model
model = tf.keras.Sequential([
layers.Dense(10, activation='softmax', input_shape=(32,)),
layers.Dense(10, activation='softmax')
])
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.fit(data, labels, batch_size=32, epoc... | _____no_output_____ | Apache-2.0 | site/el/guide/keras/overview.ipynb | KarimaTouati/docs-l10n |
Για να μάθετε περισσότερα για την αποθήκευση και την σειριοποίηση , πατήστε [εδώ](./save_and_serialize.ipynb). "Ενθουσιώδης" εκτέλεση(Eager execution)H ["Ενθουσιώδης" εκτέλεση](https://github.com/tensorflow/docs/blob/master/site/en/guide/eager.ipynb) είναι ένα (επιτακτικό) περιβάλλον προγραμματισμού το οποίο αξιολογεί... | strategy = tf.distribute.MirroredStrategy()
with strategy.scope():
model = tf.keras.Sequential()
model.add(layers.Dense(16, activation='relu', input_shape=(10,)))
model.add(layers.Dense(1, activation='sigmoid'))
optimizer = tf.keras.optimizers.SGD(0.2)
model.compile(loss='binary_crossentropy', optimizer=op... | _____no_output_____ | Apache-2.0 | site/el/guide/keras/overview.ipynb | KarimaTouati/docs-l10n |
Στη συνέχεια , εξασκείστε το μοντέλο πάνω σε δεδομένα κατά τα γνωστά : | x = np.random.random((1024, 10))
y = np.random.randint(2, size=(1024, 1))
x = tf.cast(x, tf.float32)
dataset = tf.data.Dataset.from_tensor_slices((x, y))
dataset = dataset.shuffle(buffer_size=1024).batch(32)
model.fit(dataset, epochs=1) | _____no_output_____ | Apache-2.0 | site/el/guide/keras/overview.ipynb | KarimaTouati/docs-l10n |
Imports | !pip install gensim
!pip nltk
# Import the libraries
import gensim
from glob import glob
import pandas as pd
from tqdm import tqdm, tqdm_notebook
from gensim.test.utils import common_texts
from gensim.models.doc2vec import Doc2Vec, TaggedDocument
import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus impo... | _____no_output_____ | MIT | TOI/Doc2Vec.ipynb | aashish-jain/Social-unrest-prediction |
All the CSVs contain the following datadate, title(headline), location, text(full article) Data pre-processing for doc2vec | # Read articles
df = read_articles("../data/TOI/*.csv")
# Take training data (until 1-Jan-2019)
df = df[df["date"] < pd.to_datetime("1-Jan-2019")]
# Get the vocabulary from the given text
df['vocabulary'] = df['text'].progress_apply(generate_document_vocabulary)
#Convert the vocabulary into Tagged document for doc2vec ... | _____no_output_____ | MIT | TOI/Doc2Vec.ipynb | aashish-jain/Social-unrest-prediction |
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