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 |
|---|---|---|---|---|---|
Machine Learning Pipeline - Feature EngineeringIn the following notebooks, we will go through the implementation of each one of the steps in the Machine Learning Pipeline. We will discuss:1. Data Analysis2. **Feature Engineering**3. Feature Selection4. Model Training5. Obtaining Predictions / ScoringWe will use the ho... | # to handle datasets
import pandas as pd
import numpy as np
# for plotting
import matplotlib.pyplot as plt
# for the yeo-johnson transformation
import scipy.stats as stats
# to divide train and test set
from sklearn.model_selection import train_test_split
# feature scaling
from sklearn.preprocessing import MinMaxSc... | (1460, 81)
| BSD-3-Clause | section-04-research-and-development/02-machine-learning-pipeline-feature-engineering.ipynb | chauthinh/machine-learning-deployment |
Separate dataset into train and testIt is important to separate our data intro training and testing set. When we engineer features, some techniques learn parameters from data. It is important to learn these parameters only from the train set. This is to avoid over-fitting.Our feature engineering techniques will learn:... | # Let's separate into train and test set
# Remember to set the seed (random_state for this sklearn function)
X_train, X_test, y_train, y_test = train_test_split(
data.drop(['Id', 'SalePrice'], axis=1), # predictive variables
data['SalePrice'], # target
test_size=0.1, # portion of dataset to allocate to tes... | _____no_output_____ | BSD-3-Clause | section-04-research-and-development/02-machine-learning-pipeline-feature-engineering.ipynb | chauthinh/machine-learning-deployment |
Feature EngineeringIn the following cells, we will engineer the variables of the House Price Dataset so that we tackle:1. Missing values2. Temporal variables3. Non-Gaussian distributed variables4. Categorical variables: remove rare labels5. Categorical variables: convert strings to numbers5. Put the variables in a sim... | y_train = np.log(y_train)
y_test = np.log(y_test) | _____no_output_____ | BSD-3-Clause | section-04-research-and-development/02-machine-learning-pipeline-feature-engineering.ipynb | chauthinh/machine-learning-deployment |
Missing values Categorical variablesWe will replace missing values with the string "missing" in those variables with a lot of missing data. Alternatively, we will replace missing data with the most frequent category in those variables that contain fewer observations without values. This is common practice. | # let's identify the categorical variables
# we will capture those of type object
cat_vars = [var for var in data.columns if data[var].dtype == 'O']
# MSSubClass is also categorical by definition, despite its numeric values
# (you can find the definitions of the variables in the data_description.txt
# file available ... | _____no_output_____ | BSD-3-Clause | section-04-research-and-development/02-machine-learning-pipeline-feature-engineering.ipynb | chauthinh/machine-learning-deployment |
Numerical variablesTo engineer missing values in numerical variables, we will:- add a binary missing indicator variable- and then replace the missing values in the original variable with the mean | # now let's identify the numerical variables
num_vars = [
var for var in X_train.columns if var not in cat_vars and var != 'SalePrice'
]
# number of numerical variables
len(num_vars)
# make a list with the numerical variables that contain missing values
vars_with_na = [
var for var in num_vars
if X_train[... | _____no_output_____ | BSD-3-Clause | section-04-research-and-development/02-machine-learning-pipeline-feature-engineering.ipynb | chauthinh/machine-learning-deployment |
Temporal variables Capture elapsed timeWe learned in the previous notebook, that there are 4 variables that refer to the years in which the house or the garage were built or remodeled. We will capture the time elapsed between those variables and the year in which the house was sold: | def elapsed_years(df, var):
# capture difference between the year variable
# and the year in which the house was sold
df[var] = df['YrSold'] - df[var]
return df
for var in ['YearBuilt', 'YearRemodAdd', 'GarageYrBlt']:
X_train = elapsed_years(X_train, var)
X_test = elapsed_years(X_test, var)
# no... | _____no_output_____ | BSD-3-Clause | section-04-research-and-development/02-machine-learning-pipeline-feature-engineering.ipynb | chauthinh/machine-learning-deployment |
Numerical variable transformation Logarithmic transformationIn the previous notebook, we observed that the numerical variables are not normally distributed.We will transform with the logarightm the positive numerical variables in order to get a more Gaussian-like distribution. | for var in ["LotFrontage", "1stFlrSF", "GrLivArea"]:
X_train[var] = np.log(X_train[var])
X_test[var] = np.log(X_test[var])
# check that test set does not contain null values in the engineered variables
[var for var in ["LotFrontage", "1stFlrSF", "GrLivArea"] if X_test[var].isnull().sum() > 0]
# same for train s... | _____no_output_____ | BSD-3-Clause | section-04-research-and-development/02-machine-learning-pipeline-feature-engineering.ipynb | chauthinh/machine-learning-deployment |
Yeo-Johnson transformationWe will apply the Yeo-Johnson transformation to LotArea. | # the yeo-johnson transformation learns the best exponent to transform the variable
# it needs to learn it from the train set:
X_train['LotArea'], param = stats.yeojohnson(X_train['LotArea'])
# and then apply the transformation to the test set with the same
# parameter: see who this time we pass param as argument to ... | _____no_output_____ | BSD-3-Clause | section-04-research-and-development/02-machine-learning-pipeline-feature-engineering.ipynb | chauthinh/machine-learning-deployment |
Binarize skewed variablesThere were a few variables very skewed, we would transform those into binary variables. | skewed = [
'BsmtFinSF2', 'LowQualFinSF', 'EnclosedPorch',
'3SsnPorch', 'ScreenPorch', 'MiscVal'
]
for var in skewed:
# map the variable values into 0 and 1
X_train[var] = np.where(X_train[var]==0, 0, 1)
X_test[var] = np.where(X_test[var]==0, 0, 1) | _____no_output_____ | BSD-3-Clause | section-04-research-and-development/02-machine-learning-pipeline-feature-engineering.ipynb | chauthinh/machine-learning-deployment |
Categorical variables Apply mappingsThese are variables which values have an assigned order, related to quality. For more information, check Kaggle website. | # re-map strings to numbers, which determine quality
qual_mappings = {'Po': 1, 'Fa': 2, 'TA': 3, 'Gd': 4, 'Ex': 5, 'Missing': 0, 'NA': 0}
qual_vars = ['ExterQual', 'ExterCond', 'BsmtQual', 'BsmtCond',
'HeatingQC', 'KitchenQual', 'FireplaceQu',
'GarageQual', 'GarageCond',
]
for v... | _____no_output_____ | BSD-3-Clause | section-04-research-and-development/02-machine-learning-pipeline-feature-engineering.ipynb | chauthinh/machine-learning-deployment |
Removing Rare LabelsFor the remaining categorical variables, we will group those categories that are present in less than 1% of the observations. That is, all values of categorical variables that are shared by less than 1% of houses, well be replaced by the string "Rare".To learn more about how to handle categorical v... | # capture all quality variables
qual_vars = qual_vars + finish_vars + ['BsmtExposure','GarageFinish','Fence']
# capture the remaining categorical variables
# (those that we did not re-map)
cat_others = [
var for var in cat_vars if var not in qual_vars
]
len(cat_others)
def find_frequent_labels(df, var, rare_pe... | MSZoning Index(['FV', 'RH', 'RL', 'RM'], dtype='object', name='MSZoning')
Street Index(['Pave'], dtype='object', name='Street')
Alley Index(['Grvl', 'Missing', 'Pave'], dtype='object', name='Alley')
LotShape Index(['IR1', 'IR2', 'Reg'], dtype='object', name='LotShape')
LandContour Index(['Bnk', 'HLS', 'Low', 'Lvl']... | BSD-3-Clause | section-04-research-and-development/02-machine-learning-pipeline-feature-engineering.ipynb | chauthinh/machine-learning-deployment |
Encoding of categorical variablesNext, we need to transform the strings of the categorical variables into numbers. We will do it so that we capture the monotonic relationship between the label and the target.To learn more about how to encode categorical variables visit our course [Feature Engineering for Machine Learn... | # this function will assign discrete values to the strings of the variables,
# so that the smaller value corresponds to the category that shows the smaller
# mean house sale price
def replace_categories(train, test, y_train, var, target):
tmp = pd.concat([X_train, y_train], axis=1)
# order the catego... | _____no_output_____ | BSD-3-Clause | section-04-research-and-development/02-machine-learning-pipeline-feature-engineering.ipynb | chauthinh/machine-learning-deployment |
The monotonic relationship is particularly clear for the variables MSZoning and Neighborhood. Note how, the higher the integer that now represents the category, the higher the mean house sale price.(remember that the target is log-transformed, that is why the differences seem so small). Feature ScalingFor use in linea... | # create scaler
scaler = MinMaxScaler()
# fit the scaler to the train set
scaler.fit(X_train)
# transform the train and test set
# sklearn returns numpy arrays, so we wrap the
# array with a pandas dataframe
X_train = pd.DataFrame(
scaler.transform(X_train),
columns=X_train.columns
)
X_test = pd.DataFra... | _____no_output_____ | BSD-3-Clause | section-04-research-and-development/02-machine-learning-pipeline-feature-engineering.ipynb | chauthinh/machine-learning-deployment |
Fictional Army - Filtering and Sorting Introduction:This exercise was inspired by this [page](http://chrisalbon.com/python/)Special thanks to: https://github.com/chrisalbon for sharing the dataset and materials. Step 1. Import the necessary libraries | import pandas as pd | _____no_output_____ | BSD-3-Clause | 02_Filtering_&_Sorting/Fictional Army/Exercise_with_solutions.ipynb | ViniciusRFerraz/pandas_exercises |
Step 2. This is the data given as a dictionary | # Create an example dataframe about a fictional army
raw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks', 'Dragoons', 'Dragoons', 'Dragoons', 'Dragoons', 'Scouts', 'Scouts', 'Scouts', 'Scouts'],
'company': ['1st', '1st', '2nd', '2nd', '1st', '1st', '2nd', '2nd','1st', '1st', '2nd... | _____no_output_____ | BSD-3-Clause | 02_Filtering_&_Sorting/Fictional Army/Exercise_with_solutions.ipynb | ViniciusRFerraz/pandas_exercises |
Step 3. Create a dataframe and assign it to a variable called army. Don't forget to include the columns names in the order presented in the dictionary ('regiment', 'company', 'deaths'...) so that the column index order is consistent with the solutions. If omitted, pandas will order the columns alphabetically. | army = pd.DataFrame(raw_data, columns = ['regiment', 'company', 'deaths', 'battles', 'size', 'veterans', 'readiness', 'armored', 'deserters', 'origin']) | _____no_output_____ | BSD-3-Clause | 02_Filtering_&_Sorting/Fictional Army/Exercise_with_solutions.ipynb | ViniciusRFerraz/pandas_exercises |
Step 4. Set the 'origin' colum as the index of the dataframe | army = army.set_index('origin')
army | _____no_output_____ | BSD-3-Clause | 02_Filtering_&_Sorting/Fictional Army/Exercise_with_solutions.ipynb | ViniciusRFerraz/pandas_exercises |
Step 5. Print only the column veterans | army['veterans'] | _____no_output_____ | BSD-3-Clause | 02_Filtering_&_Sorting/Fictional Army/Exercise_with_solutions.ipynb | ViniciusRFerraz/pandas_exercises |
Step 6. Print the columns 'veterans' and 'deaths' | army[['veterans', 'deaths']] | _____no_output_____ | BSD-3-Clause | 02_Filtering_&_Sorting/Fictional Army/Exercise_with_solutions.ipynb | ViniciusRFerraz/pandas_exercises |
Step 7. Print the name of all the columns. | army.columns | _____no_output_____ | BSD-3-Clause | 02_Filtering_&_Sorting/Fictional Army/Exercise_with_solutions.ipynb | ViniciusRFerraz/pandas_exercises |
Step 8. Select the 'deaths', 'size' and 'deserters' columns from Maine and Alaska | # Select all rows with the index label "Maine" and "Alaska"
army.loc[['Maine','Alaska'] , ["deaths","size","deserters"]] | _____no_output_____ | BSD-3-Clause | 02_Filtering_&_Sorting/Fictional Army/Exercise_with_solutions.ipynb | ViniciusRFerraz/pandas_exercises |
Step 9. Select the rows 3 to 7 and the columns 3 to 6 | #
army.iloc[3:7, 3:6] | _____no_output_____ | BSD-3-Clause | 02_Filtering_&_Sorting/Fictional Army/Exercise_with_solutions.ipynb | ViniciusRFerraz/pandas_exercises |
Step 10. Select every row after the fourth row | army.iloc[3:] | _____no_output_____ | BSD-3-Clause | 02_Filtering_&_Sorting/Fictional Army/Exercise_with_solutions.ipynb | ViniciusRFerraz/pandas_exercises |
Step 11. Select every row up to the 4th row | army.iloc[:3] | _____no_output_____ | BSD-3-Clause | 02_Filtering_&_Sorting/Fictional Army/Exercise_with_solutions.ipynb | ViniciusRFerraz/pandas_exercises |
Step 12. Select the 3rd column up to the 7th column | # the first : means all
# after the comma you select the range
army.iloc[: , 4:7] | _____no_output_____ | BSD-3-Clause | 02_Filtering_&_Sorting/Fictional Army/Exercise_with_solutions.ipynb | ViniciusRFerraz/pandas_exercises |
Step 13. Select rows where df.deaths is greater than 50 | army[army['deaths'] > 50] | _____no_output_____ | BSD-3-Clause | 02_Filtering_&_Sorting/Fictional Army/Exercise_with_solutions.ipynb | ViniciusRFerraz/pandas_exercises |
Step 14. Select rows where df.deaths is greater than 500 or less than 50 | army[(army['deaths'] > 500) | (army['deaths'] < 50)] | _____no_output_____ | BSD-3-Clause | 02_Filtering_&_Sorting/Fictional Army/Exercise_with_solutions.ipynb | ViniciusRFerraz/pandas_exercises |
Step 15. Select all the regiments not named "Dragoons" | army[(army['regiment'] != 'Dragoons')] | _____no_output_____ | BSD-3-Clause | 02_Filtering_&_Sorting/Fictional Army/Exercise_with_solutions.ipynb | ViniciusRFerraz/pandas_exercises |
Step 16. Select the rows called Texas and Arizona | army.loc[['Arizona', 'Texas']] | _____no_output_____ | BSD-3-Clause | 02_Filtering_&_Sorting/Fictional Army/Exercise_with_solutions.ipynb | ViniciusRFerraz/pandas_exercises |
Step 17. Select the third cell in the row named Arizona | army.loc[['Arizona'], ['deaths']]
#OR
army.iloc[[0], army.columns.get_loc('deaths')] | _____no_output_____ | BSD-3-Clause | 02_Filtering_&_Sorting/Fictional Army/Exercise_with_solutions.ipynb | ViniciusRFerraz/pandas_exercises |
Step 18. Select the third cell down in the column named deaths | army.loc['Texas', 'deaths']
#OR
army.iloc[[2], army.columns.get_loc('deaths')]
| _____no_output_____ | BSD-3-Clause | 02_Filtering_&_Sorting/Fictional Army/Exercise_with_solutions.ipynb | ViniciusRFerraz/pandas_exercises |
Title Spread Element Dependencies Matplotlib Backends Matplotlib Bokeh | import numpy as np
import holoviews as hv
hv.extension('matplotlib') | _____no_output_____ | BSD-3-Clause | examples/reference/elements/matplotlib/Spread.ipynb | stonebig/holoviews |
``Spread`` elements have the same data format as the [``ErrorBars``](ErrorBars.ipynb) element, namely x- and y-values with associated symmetric or asymmetric errors, but are interpreted as samples from a continuous distribution (just as ``Curve`` is the continuous version of ``Scatter``). These are often paired with a... | np.random.seed(42)
xs = np.linspace(0, np.pi*2, 20)
err = 0.2+np.random.rand(len(xs))
hv.Spread((xs, np.sin(xs), err)) | _____no_output_____ | BSD-3-Clause | examples/reference/elements/matplotlib/Spread.ipynb | stonebig/holoviews |
Asymmetric Given three value dimensions corresponding to the position on the y-axis, the negative error and the positive error, ``Spread`` can be used to visualize assymmetric errors: | %%opts Spread (facecolor='indianred' alpha=1)
xs = np.linspace(0, np.pi*2, 20)
hv.Spread((xs, np.sin(xs), 0.1+np.random.rand(len(xs)), 0.1+np.random.rand(len(xs))),
vdims=['y', 'yerrneg', 'yerrpos']) | _____no_output_____ | BSD-3-Clause | examples/reference/elements/matplotlib/Spread.ipynb | stonebig/holoviews |
Run in Colab View on GitHub Vertex AI: Track parameters and metrics for custom training jobs OverviewThis notebook demonstrates how to track metrics and parameters for Vertex AI custom training jobs, and how to perform detailed analysis using this data. DatasetThis example use... | import sys
if "google.colab" in sys.modules:
USER_FLAG = ""
else:
USER_FLAG = "--user"
!python3 -m pip install {USER_FLAG} google-cloud-aiplatform --upgrade | _____no_output_____ | Apache-2.0 | ai-platform-unified/notebooks/unofficial/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb | thepycoder/ai-platform-samples |
Restart the kernelAfter you install the additional packages, you need to restart the notebook kernel so it can find the packages. | # Automatically restart kernel after installs
import os
if not os.getenv("IS_TESTING"):
# Automatically restart kernel after installs
import IPython
app = IPython.Application.instance()
app.kernel.do_shutdown(True) | _____no_output_____ | Apache-2.0 | ai-platform-unified/notebooks/unofficial/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb | thepycoder/ai-platform-samples |
Before you begin Select a GPU runtime**Make sure you're running this notebook in a GPU runtime if you have that option. In Colab, select "Runtime --> Change runtime type > GPU"** Set up your Google Cloud project**The following steps are required, regardless of your notebook environment.**1. [Select or create a Google... | import os
PROJECT_ID = ""
# Get your Google Cloud project ID from gcloud
if not os.getenv("IS_TESTING"):
shell_output=!gcloud config list --format 'value(core.project)' 2>/dev/null
PROJECT_ID = shell_output[0]
print("Project ID: ", PROJECT_ID) | _____no_output_____ | Apache-2.0 | ai-platform-unified/notebooks/unofficial/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb | thepycoder/ai-platform-samples |
Otherwise, set your project ID here. | if PROJECT_ID == "" or PROJECT_ID is None:
PROJECT_ID = "[your-project-id]" # @param {type:"string"} | _____no_output_____ | Apache-2.0 | ai-platform-unified/notebooks/unofficial/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb | thepycoder/ai-platform-samples |
Set gcloud config to your project ID. | !gcloud config set project $PROJECT_ID | _____no_output_____ | Apache-2.0 | ai-platform-unified/notebooks/unofficial/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb | thepycoder/ai-platform-samples |
TimestampIf you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append it onto the name of resources you create in this tutorial. | from datetime import datetime
TIMESTAMP = datetime.now().strftime("%Y%m%d%H%M%S") | _____no_output_____ | Apache-2.0 | ai-platform-unified/notebooks/unofficial/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb | thepycoder/ai-platform-samples |
Authenticate your Google Cloud account**If you are using Google Cloud Notebooks**, your environment is alreadyauthenticated. Skip this step. **If you are using Colab**, run the cell below and follow the instructionswhen prompted to authenticate your account via oAuth.**Otherwise**, follow these steps:1. In the Cloud C... | import os
import sys
# If you are running this notebook in Colab, run this cell and follow the
# instructions to authenticate your GCP account. This provides access to your
# Cloud Storage bucket and lets you submit training jobs and prediction
# requests.
# If on Google Cloud Notebooks, then don't execute this code
... | _____no_output_____ | Apache-2.0 | ai-platform-unified/notebooks/unofficial/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb | thepycoder/ai-platform-samples |
Create a Cloud Storage bucket**The following steps are required, regardless of your notebook environment.**When you submit a training job using the Cloud SDK, you upload a Python packagecontaining your training code to a Cloud Storage bucket. Vertex AI runsthe code from this package. In this tutorial, Vertex AI also s... | BUCKET_NAME = "gs://[your-bucket-name]" # @param {type:"string"}
REGION = "[your-region]" # @param {type:"string"}
if BUCKET_NAME == "" or BUCKET_NAME is None or BUCKET_NAME == "gs://[your-bucket-name]":
BUCKET_NAME = "gs://" + PROJECT_ID + "-aip-" + TIMESTAMP | _____no_output_____ | Apache-2.0 | ai-platform-unified/notebooks/unofficial/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb | thepycoder/ai-platform-samples |
**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket. | ! gsutil mb -l $REGION $BUCKET_NAME | _____no_output_____ | Apache-2.0 | ai-platform-unified/notebooks/unofficial/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb | thepycoder/ai-platform-samples |
Finally, validate access to your Cloud Storage bucket by examining its contents: | ! gsutil ls -al $BUCKET_NAME | _____no_output_____ | Apache-2.0 | ai-platform-unified/notebooks/unofficial/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb | thepycoder/ai-platform-samples |
Import libraries and define constants Import required libraries. | import pandas as pd
from google.cloud import aiplatform
from sklearn.metrics import mean_absolute_error, mean_squared_error
from tensorflow.python.keras.utils import data_utils | _____no_output_____ | Apache-2.0 | ai-platform-unified/notebooks/unofficial/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb | thepycoder/ai-platform-samples |
Initialize Vertex AI and set an _experiment_ Define experiment name. | EXPERIMENT_NAME = "" # @param {type:"string"} | _____no_output_____ | Apache-2.0 | ai-platform-unified/notebooks/unofficial/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb | thepycoder/ai-platform-samples |
If EXEPERIMENT_NAME is not set, set a default one below: | if EXPERIMENT_NAME == "" or EXPERIMENT_NAME is None:
EXPERIMENT_NAME = "my-experiment-" + TIMESTAMP | _____no_output_____ | Apache-2.0 | ai-platform-unified/notebooks/unofficial/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb | thepycoder/ai-platform-samples |
Initialize the *client* for Vertex AI. | aiplatform.init(
project=PROJECT_ID,
location=REGION,
staging_bucket=BUCKET_NAME,
experiment=EXPERIMENT_NAME,
) | _____no_output_____ | Apache-2.0 | ai-platform-unified/notebooks/unofficial/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb | thepycoder/ai-platform-samples |
Tracking parameters and metrics in Vertex AI custom training jobs This example uses the Abalone Dataset. For more information about this dataset please visit: https://archive.ics.uci.edu/ml/datasets/abalone | !wget https://storage.googleapis.com/download.tensorflow.org/data/abalone_train.csv
!gsutil cp abalone_train.csv {BUCKET_NAME}/data/
gcs_csv_path = f"{BUCKET_NAME}/data/abalone_train.csv" | _____no_output_____ | Apache-2.0 | ai-platform-unified/notebooks/unofficial/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb | thepycoder/ai-platform-samples |
Create a managed tabular dataset from a CSVA Managed dataset can be used to create an AutoML model or a custom model. | ds = aiplatform.TabularDataset.create(display_name="abalone", gcs_source=[gcs_csv_path])
ds.resource_name | _____no_output_____ | Apache-2.0 | ai-platform-unified/notebooks/unofficial/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb | thepycoder/ai-platform-samples |
Write the training scriptRun the following cell to create the training script that is used in the sample custom training job. | %%writefile training_script.py
import pandas as pd
import argparse
import os
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
parser = argparse.ArgumentParser()
parser.add_argument('--epochs', dest='epochs',
default=10, type=int,
help='Nu... | _____no_output_____ | Apache-2.0 | ai-platform-unified/notebooks/unofficial/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb | thepycoder/ai-platform-samples |
Launch a custom training job and track its trainig parameters on Vertex AI ML Metadata | job = aiplatform.CustomTrainingJob(
display_name="train-abalone-dist-1-replica",
script_path="training_script.py",
container_uri="gcr.io/cloud-aiplatform/training/tf-cpu.2-2:latest",
requirements=["gcsfs==0.7.1"],
model_serving_container_image_uri="gcr.io/cloud-aiplatform/prediction/tf2-cpu.2-2:late... | _____no_output_____ | Apache-2.0 | ai-platform-unified/notebooks/unofficial/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb | thepycoder/ai-platform-samples |
Start a new experiment run to track training parameters and start the training job. Note that this operation will take around 10 mins. | aiplatform.start_run("custom-training-run-1") # Change this to your desired run name
parameters = {"epochs": 10, "num_units": 64}
aiplatform.log_params(parameters)
model = job.run(
ds,
replica_count=1,
model_display_name="abalone-model",
args=[f"--epochs={parameters['epochs']}", f"--num_units={paramet... | _____no_output_____ | Apache-2.0 | ai-platform-unified/notebooks/unofficial/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb | thepycoder/ai-platform-samples |
Deploy Model and calculate prediction metrics Deploy model to Google Cloud. This operation will take 10-20 mins. | endpoint = model.deploy(machine_type="n1-standard-4") | _____no_output_____ | Apache-2.0 | ai-platform-unified/notebooks/unofficial/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb | thepycoder/ai-platform-samples |
Once model is deployed, perform online prediction using the `abalone_test` dataset and calculate prediction metrics. Prepare the prediction dataset. | def read_data(uri):
dataset_path = data_utils.get_file("auto-mpg.data", uri)
col_names = [
"Length",
"Diameter",
"Height",
"Whole weight",
"Shucked weight",
"Viscera weight",
"Shell weight",
"Age",
]
dataset = pd.read_csv(
dataset_p... | _____no_output_____ | Apache-2.0 | ai-platform-unified/notebooks/unofficial/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb | thepycoder/ai-platform-samples |
Perform online prediction. | prediction = endpoint.predict(test_dataset.tolist())
prediction | _____no_output_____ | Apache-2.0 | ai-platform-unified/notebooks/unofficial/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb | thepycoder/ai-platform-samples |
Calculate and track prediction evaluation metrics. | mse = mean_squared_error(test_labels, prediction.predictions)
mae = mean_absolute_error(test_labels, prediction.predictions)
aiplatform.log_metrics({"mse": mse, "mae": mae}) | _____no_output_____ | Apache-2.0 | ai-platform-unified/notebooks/unofficial/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb | thepycoder/ai-platform-samples |
Extract all parameters and metrics created during this experiment. | aiplatform.get_experiment_df() | _____no_output_____ | Apache-2.0 | ai-platform-unified/notebooks/unofficial/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb | thepycoder/ai-platform-samples |
View data in the Cloud Console Parameters and metrics can also be viewed in the Cloud Console. | print("Vertex AI Experiments:")
print(
f"https://console.cloud.google.com/ai/platform/experiments/experiments?folder=&organizationId=&project={PROJECT_ID}"
) | _____no_output_____ | Apache-2.0 | ai-platform-unified/notebooks/unofficial/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb | thepycoder/ai-platform-samples |
Cleaning upTo clean up all Google Cloud resources used in this project, you can [delete the Google Cloudproject](https://cloud.google.com/resource-manager/docs/creating-managing-projectsshutting_down_projects) you used for the tutorial.Otherwise, you can delete the individual resources you created in this tutorial:Tra... | delete_training_job = True
delete_model = True
delete_endpoint = True
# Warning: Setting this to true will delete everything in your bucket
delete_bucket = False
# Delete the training job
job.delete()
# Delete the model
model.delete()
# Delete the endpoint
endpoint.delete()
if delete_bucket and "BUCKET_NAME" in gl... | _____no_output_____ | Apache-2.0 | ai-platform-unified/notebooks/unofficial/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb | thepycoder/ai-platform-samples |
End-To-End Example: Password ProgramPassword Program:- 5 attempts for the password- On correct password, print: “Access Granted”, then end the program - On incorrect password “Invalid Password Attempt ” and give the user another try- After 5 attempts, print “You are locked out”. Then end the program. | secret = "rhubarb"
attempts = 0
while True:
password = input("Enter Password: ")
attempts= attempts + 1
if password == secret:
print("Access Granted!")
break
print("Invalid password attempt #",attempts)
if attempts == 5:
print("You are locked out")
break | Enter Password: sd
Invalid password attempt # 1
Enter Password: fds
Invalid password attempt # 2
Enter Password: sd
Invalid password attempt # 3
Enter Password: d
Invalid password attempt # 4
Enter Password: d
Invalid password attempt # 5
You are locked out
| MIT | content/lessons/04/End-To-End-Example/ETEE-Password-Program.ipynb | MahopacHS/spring-2020-Lamk0810 |
Final Lab*Felix Rojo Lapalma* Main taskIn this notebook, we will apply transfer learning techniques to finetune the [MobileNet](https://arxiv.org/pdf/1704.04861.pdf) CNN on [Cifar-10](https://www.cs.toronto.edu/~kriz/cifar.html) dataset. ProceduresIn general, the main steps that we will follow are:1. Load data, analyz... | # load libs
import os
import matplotlib.pyplot as plt
from IPython.display import SVG
# https://keras.io/applications/#documentation-for-individual-models
from keras.applications.mobilenet import MobileNet
from keras.datasets import cifar10
from keras.models import Model
from keras.utils.vis_utils import model_to_dot
f... | Using TensorFlow backend.
| MIT | vpc_2018/lab/Lab_VpC_FelixRojoLapalma_003.ipynb | felixlapalma/diplodatos_2018 |
cuda | cuda_flag=False
if cuda_flag:
# Setup one GPU for tensorflow (don't be greedy).
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
# The GPU id to use, "0", "1", etc.
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
# Limit tensorflow gpu usage.
# Maybe you should comment this lines if you run tensorflow ... | _____no_output_____ | MIT | vpc_2018/lab/Lab_VpC_FelixRojoLapalma_003.ipynb | felixlapalma/diplodatos_2018 |
1. Load data, analyze and split in *training*/*validation*/*testing* sets | # Cifar-10 class names
# We will create a dictionary for each type of label
# This is a mapping from the int class name to
# their corresponding string class name
LABELS = {
0: "airplane",
1: "automobile",
2: "bird",
3: "cat",
4: "deer",
5: "dog",
6: "frog",
7: "horse",
8: "ship",
... | Train
airplane : 5000 or 10.00%
automobile : 5000 or 10.00%
bird : 5000 or 10.00%
cat : 5000 or 10.00%
deer : 5000 or 10.00%
dog : 5000 or 10.00%
frog : 5000 or 10.00%
horse : 5000 or 10.00%
ship : 5000 or 10.00%
truck ... | MIT | vpc_2018/lab/Lab_VpC_FelixRojoLapalma_003.ipynb | felixlapalma/diplodatos_2018 |
Todo parece ir de acuerdo a la documentación. Veamos las imagenes, | from genlib import sample_images_data,plot_sample_images
for xy,yt in zip([(x_train_data,y_train_data.flatten()),(x_test_data,y_test_data.flatten())],['Train','Test']):
print('{:>15s}'.format(yt))
train_sample_images, train_sample_labels = sample_images_data(*xy,LABELS)
plot_sample_images(train_sample_image... | Cifar-10 x_train shape: (20000, 32, 32, 3)
Cifar-10 y_train shape: (20000, 1)
Cifar-10 x_val shape: (4000, 32, 32, 3)
Cifar-10 y_val shape: (4000, 1)
Cifar-10 x_test shape: (10000, 32, 32, 3)
Cifar-10 y_test shape: (10000, 1)
| MIT | vpc_2018/lab/Lab_VpC_FelixRojoLapalma_003.ipynb | felixlapalma/diplodatos_2018 |
Veamos si quedaron balanceados Train y Validation | for y,yt in zip([y_train_data.flatten(),y_val_data.flatten()],['Train','Validation']):
print('{:>15s}'.format(yt))
get_classes_distribution(y,LABELS)
plot_label_per_class(y,LABELS)
# In order to use the MobileNet CNN pre-trained on imagenet, we have
# to resize our images to have one of the following static... | _____no_output_____ | MIT | vpc_2018/lab/Lab_VpC_FelixRojoLapalma_003.ipynb | felixlapalma/diplodatos_2018 |
2. Load CNN and analyze architecture | #Model
NO_EPOCHS = 25
BATCH_SIZE = 32
NET_IMG_ROWS = 128
NET_IMG_COLS = 128
############
# [COMPLETE]
# Use the MobileNet class from Keras to load your base model, pre-trained on imagenet.
# We wan't to load the pre-trained weights, but without the classification layer.
# Check the notebook '3_transfer-learning' or ht... | _____no_output_____ | MIT | vpc_2018/lab/Lab_VpC_FelixRojoLapalma_003.ipynb | felixlapalma/diplodatos_2018 |
3. Adapt this CNN to our problem | ############
# [COMPLETE]
# Having the CNN loaded, now we have to add some layers to adapt this network to our
# classification problem.
# We can choose to finetune just the new added layers, some particular layers or all the layer of the
# model. Play with different settings and compare the results.
############
# g... | _________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, 128, 128, 3) 0
________________________________________________________... | MIT | vpc_2018/lab/Lab_VpC_FelixRojoLapalma_003.ipynb | felixlapalma/diplodatos_2018 |
4. Setup data augmentation techniques | ############
# [COMPLETE]
# Use data augmentation to train your model.
# Use the Keras ImageDataGenerator class for this porpouse.
# Note: Given that we want to load our images from disk, instead of using
# ImageDataGenerator.flow method, we have to use ImageDataGenerator.flow_from_directory
# method in the followin... | Found 40000 images belonging to 10 classes.
Found 10000 images belonging to 10 classes.
| MIT | vpc_2018/lab/Lab_VpC_FelixRojoLapalma_003.ipynb | felixlapalma/diplodatos_2018 |
5. Add some keras callbacks | ############
# [COMPLETE]
# Load and set some Keras callbacks here!
############
EXP_ID='experiment_003/'
from keras.callbacks import ModelCheckpoint, TensorBoard
if not os.path.exists(EXP_ID):
os.makedirs(EXP_ID)
callbacks = [
ModelCheckpoint(filepath=os.path.join(EXP_ID, 'weights.{epoch:02d}-{val_loss:.2... | _____no_output_____ | MIT | vpc_2018/lab/Lab_VpC_FelixRojoLapalma_003.ipynb | felixlapalma/diplodatos_2018 |
6. Setup optimization algorithm with their hyperparameters | ############
# [COMPLETE]
# Choose some optimization algorithm and explore different hyperparameters.
# Compile your model.
############
from keras.optimizers import SGD
from keras.losses import categorical_crossentropy
#model.compile(optimizer=SGD(lr=0.0001, momentum=0.9),
# loss='categorical_crossentro... | _____no_output_____ | MIT | vpc_2018/lab/Lab_VpC_FelixRojoLapalma_003.ipynb | felixlapalma/diplodatos_2018 |
7. Train model! | generator_train.n
############
# [COMPLETE]
# Use fit_generator to train your model.
# e.g.:
# model.fit_generator(
# generator_train,
# epochs=50,
# validation_data=generator_val,
# steps_per_epoch=generator_train.n // 32,
# validation_steps=generator_val.n // 32)
############
if... | Epoch 1/25
625/625 [==============================] - 1911s 3s/step - loss: 0.7648 - acc: 0.7352 - val_loss: 2.4989 - val_acc: 0.2167
Epoch 00001: saving model to experiment_003/weights.01-2.50.hdf5
Epoch 2/25
625/625 [==============================] - 1927s 3s/step - loss: 0.7447 - acc: 0.7426 - val_loss: 2.7681 - va... | MIT | vpc_2018/lab/Lab_VpC_FelixRojoLapalma_003.ipynb | felixlapalma/diplodatos_2018 |
8. Choose best model/snapshot | ############
# [COMPLETE]
# Analyze and compare your results. Choose the best model and snapshot,
# justify your election.
############
| _____no_output_____ | MIT | vpc_2018/lab/Lab_VpC_FelixRojoLapalma_003.ipynb | felixlapalma/diplodatos_2018 |
9. Evaluate final model on the *testing* set | ############
# [COMPLETE]
# Evaluate your model on the testing set.
############
| _____no_output_____ | MIT | vpc_2018/lab/Lab_VpC_FelixRojoLapalma_003.ipynb | felixlapalma/diplodatos_2018 |
导入库 | import pandas as pd
import numpy as np
from sklearn.svm import LinearSVR, LinearSVC
from sklearn.svm import *
from sklearn.linear_model import Lasso, LogisticRegression, LinearRegression
from sklearn.tree import DecisionTreeRegressor,DecisionTreeClassifier
from sklearn.ensemble import RandomForestRegressor, Rando... | _____no_output_____ | MIT | Project-2/FinalProjectFeatureSelection.ipynb | JasonCZH4/SCNU-CS-2018-DataMining |
读取数据集 | filePath = './data/138rows_after.xlsx'
dataFrame = pd.read_excel(filePath)
dataArray = np.array(dataFrame)
dataFrame | _____no_output_____ | MIT | Project-2/FinalProjectFeatureSelection.ipynb | JasonCZH4/SCNU-CS-2018-DataMining |
获取标签列 | name = [column for column in dataFrame]
name = name[5:]
pd.DataFrame(name) | _____no_output_____ | MIT | Project-2/FinalProjectFeatureSelection.ipynb | JasonCZH4/SCNU-CS-2018-DataMining |
查看数据规模 | X_withLabel = dataArray[:92,5:]
X_all = dataArray[:,5:]
y_data = dataArray[:92,3]
y_label= dataArray[:92,4].astype(int)
print("有标签数据的规模：",X_withLabel.shape)
print("所有数据的规模：",X_all.shape)
print("回归标签的规模：",y_data.shape)
print("分类标签的规模：",y_label.shape) | 有标签数据的规模： (92, 76)
所有数据的规模： (138, 76)
回归标签的规模： (92,)
分类标签的规模： (92,)
| MIT | Project-2/FinalProjectFeatureSelection.ipynb | JasonCZH4/SCNU-CS-2018-DataMining |
回归 利用Lasso进行特征选择 | lasso = Lasso(alpha = 0.5,max_iter=5000).fit(X_withLabel, y_data)
modelLasso = SelectFromModel(lasso, prefit=True)
X_Lasso = modelLasso.transform(X_withLabel)
LassoIndexMask = modelLasso.get_support() # 获取筛选的mask
value = X_withLabel[:,LassoIndexMask].tolist() # 被筛选出来的列的值
LassoIndexMask = LassoIndexMask.tolist() ... | 被筛选后剩下的特征：
1 : FP1-A1 θ 节律, µV
2 : FP1-A1 α 节律, µV
3 : FP2-A2 δ 节律,µV
4 : FP2-A2 θ 节律, µV
5 : FP2-A2 α 节律, µV
6 : FP2-A2 β(LF)节律, µV
7 : F3-A1 α 节律, µV
8 : F4-A2 α 节律, µV
9 : FZ-A2 δ 节律,µV
10 : C3-A1 α 节律, µV
11 : C4-A2 θ 节律, µV
12 : C4-A2 α 节律, µV
13 : C4-A2 β(LF)节律, µV
14 : CZ-A1 α 节律, µV
15 : P3-A1 δ 节律,µV
16 : P4-A... | MIT | Project-2/FinalProjectFeatureSelection.ipynb | JasonCZH4/SCNU-CS-2018-DataMining |
利用SVR进行特征选择 | lsvr = LinearSVR(C=10,max_iter=10000,loss='squared_epsilon_insensitive',dual=False).fit(X_withLabel, y_data)
modelLSVR = SelectFromModel(lsvr, prefit=True)
X_LSVR = modelLSVR.transform(X_withLabel)
SVRIndexMask = modelLSVR.get_support() # 获取筛选的mask
value = X_withLabel[:,SVRIndexMask].tolist() # 被筛选出来的列的值
SVRInde... | 被筛选后剩下的特征：
1 : FP1-A1 θ 节律, µV
2 : FP1-A1 β(LF)节律, µV
3 : FP2-A2 δ 节律,µV
4 : FP2-A2 θ 节律, µV
5 : FP2-A2 β(LF)节律, µV
6 : F3-A1 θ 节律, µV
7 : F4-A2 β(LF)节律, µV
8 : C3-A1 β(LF)节律, µV
9 : CZ-A1 θ 节律, µV
10 : CZ-A1 β(LF)节律, µV
11 : P3-A1 δ 节律,µV
12 : P3-A1 θ 节律, µV
13 : P3-A1 α 节律, µV
14 : P4-A2 δ 节律,µV
15 : P4-A2 θ 节律, µV
1... | MIT | Project-2/FinalProjectFeatureSelection.ipynb | JasonCZH4/SCNU-CS-2018-DataMining |
利用树进行特征选择 | decisionTree = DecisionTreeRegressor(min_samples_leaf=1,random_state=1).fit(X_withLabel, y_data)
modelDecisionTree = SelectFromModel(decisionTree, prefit=True)
X_DecisionTree = modelDecisionTree.transform(X_withLabel)
decisionTreeIndexMask = modelDecisionTree.get_support() # 获取筛选的mask
value = X_withLabel[:,LassoI... | 被筛选后剩下的特征：
1 : F4-A2 θ 节律, µV
2 : F4-A2 α 节律, µV
3 : FZ-A2 θ 节律, µV
4 : FZ-A2 β(LF)节律, µV
5 : C3-A1 θ 节律, µV
6 : C3-A1 β(LF)节律, µV
7 : CZ-A1 β(LF)节律, µV
8 : P3-A1 δ 节律,µV
9 : P3-A1 β(LF)节律, µV
10 : PZ-A2 α 节律, µV
11 : O2-A2 δ 节律,µV
12 : O2-A2 α 节律, µV
13 : F8-A2 δ 节律,µV
14 : T3-A1 θ 节律, µV
15 : T5-A1 β(LF)节律, µV
16 : T... | MIT | Project-2/FinalProjectFeatureSelection.ipynb | JasonCZH4/SCNU-CS-2018-DataMining |
利用随机森林进行特征选择 | randomForest = RandomForestRegressor().fit(X_withLabel, y_data)
modelrandomForest = SelectFromModel(randomForest, prefit=True)
X_randomForest = modelrandomForest.transform(X_withLabel)
randomForestIndexMask = modelrandomForest.get_support() # 获取筛选的mask
value = X_withLabel[:,randomForestIndexMask].tolist() # 被筛选出来... | 被筛选后剩下的特征：
1 : FP1-A1 θ 节律, µV
2 : FP1-A1 α 节律, µV
3 : FP2-A2 θ 节律, µV
4 : FP2-A2 β(LF)节律, µV
5 : F3-A1 θ 节律, µV
6 : F4-A2 θ 节律, µV
7 : C3-A1 θ 节律, µV
8 : C4-A2 δ 节律,µV
9 : C4-A2 θ 节律, µV
10 : P3-A1 δ 节律,µV
11 : P4-A2 θ 节律, µV
12 : PZ-A2 β(LF)节律, µV
13 : O1-A1 θ 节律, µV
14 : O2-A2 δ 节律,µV
15 : O2-A2 θ 节律, µV
16 : O2-A2 ... | MIT | Project-2/FinalProjectFeatureSelection.ipynb | JasonCZH4/SCNU-CS-2018-DataMining |
利用GBDT进行特征选择 | GBDTRegressor = GradientBoostingRegressor().fit(X_withLabel, y_data)
modelGBDTRegressor = SelectFromModel(GBDTRegressor, prefit=True)
X_GBDTRegressor = modelGBDTRegressor.transform(X_withLabel)
GBDTRegressorIndexMask = modelGBDTRegressor.get_support() # 获取筛选的mask
value = X_withLabel[:,GBDTRegressorIndexMask].toli... | 被筛选后剩下的特征：
1 : FP2-A2 θ 节律, µV
2 : FP2-A2 β(LF)节律, µV
3 : F3-A1 θ 节律, µV
4 : C3-A1 δ 节律,µV
5 : C3-A1 θ 节律, µV
6 : C4-A2 δ 节律,µV
7 : C4-A2 θ 节律, µV
8 : CZ-A1 θ 节律, µV
9 : P3-A1 δ 节律,µV
10 : P3-A1 α 节律, µV
11 : P4-A2 θ 节律, µV
12 : P4-A2 α 节律, µV
13 : PZ-A2 α 节律, µV
14 : PZ-A2 β(LF)节律, µV
15 : O1-A1 θ 节律, µV
16 : O2-A2 δ ... | MIT | Project-2/FinalProjectFeatureSelection.ipynb | JasonCZH4/SCNU-CS-2018-DataMining |
分类 利用Lasso进行特征选择 | lasso = Lasso(alpha = 0.3,max_iter=5000).fit(X_withLabel, y_label)
modelLasso = SelectFromModel(lasso, prefit=True)
X_Lasso = modelLasso.transform(X_withLabel)
LassoIndexMask = modelLasso.get_support() # 获取筛选的mask
value = X_withLabel[:,LassoIndexMask].tolist() # 被筛选出来的列的值
LassoIndexMask = LassoIndexMask.tolist()... | 被筛选后剩下的特征：
1 : FP1-A1 α 节律, µV
2 : FZ-A2 δ 节律,µV
3 : C4-A2 δ 节律,µV
4 : CZ-A1 α 节律, µV
5 : P3-A1 δ 节律,µV
6 : P4-A2 α 节律, µV
7 : PZ-A2 δ 节律,µV
8 : O2-A2 δ 节律,µV
9 : F7-A1 δ 节律,µV
10 : F7-A1 α 节律, µV
11 : T3-A1 α 节律, µV
12 : T4-A2 δ 节律,µV
13 : T4-A2 α 节律, µV
14 : T5-A1 δ 节律,µV
被筛选后去掉的特征：
1 : FP1-A1 δ 节律,µV
2 : FP1-A1 θ 节... | MIT | Project-2/FinalProjectFeatureSelection.ipynb | JasonCZH4/SCNU-CS-2018-DataMining |
利用SVC进行特征选择 | lsvc = LinearSVC(C=10,max_iter=10000,dual=False).fit(X_withLabel, y_label.ravel())
modelLSVC = SelectFromModel(lsvc, prefit=True)
X_LSVR = modelLSVR.transform(X_withLabel)
SVCIndexMask = modelLSVC.get_support() # 获取筛选的mask
value = X_withLabel[:,SVCIndexMask].tolist() # 被筛选出来的列的值
SVCIndexMask = SVCIndexMask.tolis... | 被筛选后剩下的特征：
1 : FP1-A1 θ 节律, µV
2 : FP2-A2 θ 节律, µV
3 : FP2-A2 α 节律, µV
4 : FP2-A2 β(LF)节律, µV
5 : FZ-A2 β(LF)节律, µV
6 : C3-A1 θ 节律, µV
7 : C3-A1 β(LF)节律, µV
8 : C4-A2 δ 节律,µV
9 : C4-A2 θ 节律, µV
10 : C4-A2 α 节律, µV
11 : CZ-A1 δ 节律,µV
12 : CZ-A1 θ 节律, µV
13 : CZ-A1 α 节律, µV
14 : P3-A1 β(LF)节律, µV
15 : P4-A2 θ 节律, µV
16 :... | MIT | Project-2/FinalProjectFeatureSelection.ipynb | JasonCZH4/SCNU-CS-2018-DataMining |
利用树进行特征选择 | decisionTree = DecisionTreeClassifier(random_state=1).fit(X_withLabel, y_label)
modelDecisionTree = SelectFromModel(decisionTree, prefit=True)
X_DecisionTree = modelDecisionTree.transform(X_withLabel)
decisionTreeIndexMask = modelDecisionTree.get_support() # 获取筛选的mask
value = X_withLabel[:,LassoIndexMask].tolist(... | 被筛选后剩下的特征：
1 : FP1-A1 δ 节律,µV
2 : FP1-A1 α 节律, µV
3 : F3-A1 θ 节律, µV
4 : C3-A1 θ 节律, µV
5 : CZ-A1 δ 节律,µV
6 : CZ-A1 β(LF)节律, µV
7 : P3-A1 α 节律, µV
8 : PZ-A2 β(LF)节律, µV
9 : O2-A2 δ 节律,µV
10 : O2-A2 β(LF)节律, µV
11 : F7-A1 θ 节律, µV
12 : T4-A2 δ 节律,µV
13 : T5-A1 α 节律, µV
14 : T6-A2 α 节律, µV
被筛选后去掉的特征：
1 : FP1-A1 θ 节律, µV... | MIT | Project-2/FinalProjectFeatureSelection.ipynb | JasonCZH4/SCNU-CS-2018-DataMining |
利用随机森林进行特征选择 | randomForest = RandomForestRegressor().fit(X_withLabel, y_label)
modelrandomForest = SelectFromModel(randomForest, prefit=True)
X_randomForest = modelrandomForest.transform(X_withLabel)
randomForestIndexMask = modelrandomForest.get_support() # 获取筛选的mask
value = X_withLabel[:,randomForestIndexMask].tolist() # 被筛选出... | 被筛选后剩下的特征：
1 : FP1-A1 θ 节律, µV
2 : FP2-A2 β(LF)节律, µV
3 : F4-A2 α 节律, µV
4 : F4-A2 β(LF)节律, µV
5 : FZ-A2 β(LF)节律, µV
6 : C3-A1 β(LF)节律, µV
7 : C4-A2 δ 节律,µV
8 : C4-A2 θ 节律, µV
9 : C4-A2 α 节律, µV
10 : CZ-A1 α 节律, µV
11 : P3-A1 δ 节律,µV
12 : P3-A1 α 节律, µV
13 : P3-A1 β(LF)节律, µV
14 : P4-A2 δ 节律,µV
15 : P4-A2 θ 节律, µV
16 :... | MIT | Project-2/FinalProjectFeatureSelection.ipynb | JasonCZH4/SCNU-CS-2018-DataMining |
利用GBDT进行特征选择 | GBDTClassifier = GradientBoostingClassifier().fit(X_withLabel, y_label)
modelGBDTClassifier = SelectFromModel(GBDTClassifier, prefit=True)
X_GBDTClassifier = modelGBDTClassifier.transform(X_withLabel)
GBDTClassifierIndexMask = modelGBDTClassifier.get_support() # 获取筛选的mask
value = X_withLabel[:,GBDTClassifierIndex... | 被筛选后剩下的特征：
1 : FP1-A1 α 节律, µV
2 : FP2-A2 θ 节律, µV
3 : FP2-A2 β(LF)节律, µV
4 : C4-A2 θ 节律, µV
5 : P3-A1 α 节律, µV
6 : P4-A2 α 节律, µV
7 : P4-A2 β(LF)节律, µV
8 : PZ-A2 β(LF)节律, µV
9 : O2-A2 δ 节律,µV
10 : F7-A1 δ 节律,µV
11 : F8-A2 δ 节律,µV
12 : F8-A2 β(LF)节律, µV
13 : T3-A1 θ 节律, µV
14 : T4-A2 δ 节律,µV
15 : T4-A2 θ 节律, µV
16 : T5... | MIT | Project-2/FinalProjectFeatureSelection.ipynb | JasonCZH4/SCNU-CS-2018-DataMining |
测试选取的特征 读入PCA和LDA降维后的数据 获取特征选取后的数据 | RegressionFeatureSelection = [dataFrameOfLassoRegressionFeature,dataFrameOfLSVRegressionFeature,dataFrameOfDecisionTreeRegressionFeature,
dataFrameOfRandomForestRegressionFeature,dataFrameOfGBDTRegressionFeature]
ClassificationFeatureSelection = [dataFrameOfLassoClassificationFeature,dataFrameOfLSVC... | _____no_output_____ | MIT | Project-2/FinalProjectFeatureSelection.ipynb | JasonCZH4/SCNU-CS-2018-DataMining |
筛选回归的特征 | allMSEResult=[]
allr2Result=[]
print("LR测试结果")
for i in range(len(RegressionFeatureSelection)):
tempArray = np.array(RegressionFeatureSelection[i])[:92,:]
temp_X = tempArray[:,5:]
temp_y = tempArray[:,3]
train_X,test_X,train_y,test_y = train_test_split(temp_X,temp_y,test_size=0.2,random_state=4)
clf... | _____no_output_____ | MIT | Project-2/FinalProjectFeatureSelection.ipynb | JasonCZH4/SCNU-CS-2018-DataMining |
原始特征回归表现 | print("LR测试结果")
tempArray = dataArray[:92,:]
temp_X = tempArray[:,5:]
temp_y = tempArray[:,3].astype(int)
train_X,test_X,train_y,test_y = train_test_split(temp_X,temp_y,test_size=0.2,random_state=4)
clf=LinearRegression()
clf.fit(train_X,train_y)
pred_y = clf.predict(test_X)
print('Mean squared error: %.2f'
... | _____no_output_____ | MIT | Project-2/FinalProjectFeatureSelection.ipynb | JasonCZH4/SCNU-CS-2018-DataMining |
筛选分类的特征 | allAccuracyResult=[]
allF1Result=[]
print("LR测试结果")
for i in range(len(ClassificationFeatureSelection)):
tempArray = np.array(ClassificationFeatureSelection[i])[:92,:]
temp_X = tempArray[:,5:]
temp_y = tempArray[:,4].astype(int)
train_X,test_X,train_y,test_y = train_test_split(temp_X,temp_y,test_size=0.2... | _____no_output_____ | MIT | Project-2/FinalProjectFeatureSelection.ipynb | JasonCZH4/SCNU-CS-2018-DataMining |
原始特征分类表现 | print("LR测试结果")
tempArray = dataArray[:92,:]
temp_X = tempArray[:,5:]
temp_y = tempArray[:,4].astype(int)
train_X,test_X,train_y,test_y = train_test_split(temp_X,temp_y,test_size=0.2,random_state=4)
clf=LogisticRegression(max_iter=10000)
clf.fit(train_X,train_y)
pred_y = clf.predict(test_X)
print('Accuracy: %.2... | _____no_output_____ | MIT | Project-2/FinalProjectFeatureSelection.ipynb | JasonCZH4/SCNU-CS-2018-DataMining |
CSX46: Class session 2 *Introduction to the igraph package and the Pathway Commons network in SIF format* Objective: load a network of human molecular interactions and create three igraph `Graph` objects from it (one for protein-protein interactions, one for metabolism interactions, and one for directed protein-protei... | pcdf <- read.table("shared/pathway_commons.sif",
sep="\t",
quote="",
comment.char="",
stringsAsFactors=FALSE,
header=FALSE,
col.names=c("species1","interaction_type","species2"))
| _____no_output_____ | Apache-2.0 | class02a_igraph_R.ipynb | curiositymap/Networks-in-Computational-Biology |
Let's take a peek at `pcdf` using the `head` function: | head(pcdf)
library(igraph)
interaction_types_ppi <- c("interacts-with",
"in-complex-with",
"neighbor-of")
interaction_types_metab <- c("controls-production-of",
"consumption-controlled-by",
"controls-produc... |
Attaching package: ‘igraph’
The following objects are masked from ‘package:stats’:
decompose, spectrum
The following object is masked from ‘package:base’:
union
| Apache-2.0 | class02a_igraph_R.ipynb | curiositymap/Networks-in-Computational-Biology |
Subset data frame `pcdf` to obtain only the rows whose interactions are in `interaction_types_ppi`, and select only columns 1 and 3: | pcdf_ppi <- pcdf[pcdf$interaction_type %in% interaction_types_ppi,c(1,3)] | _____no_output_____ | Apache-2.0 | class02a_igraph_R.ipynb | curiositymap/Networks-in-Computational-Biology |
Use the `igraph` function `graph_from_data_farme` to build a network from the edge-list data in `pcdf_ppi`; use `print` to see a summary of the graph: | graph_ppi <- graph_from_data_frame(pcdf_ppi,
directed=FALSE)
print(graph_ppi) | IGRAPH ba9e496 UN-- 17020 523498 --
+ attr: name (v/c)
+ edges from ba9e496 (vertex names):
[1] A1BG--ABCC6 A1BG--ANXA7 A1BG--CDKN1A A1BG--CRISP3 A1BG--GDPD1
[6] A1BG--GRB2 A1BG--GRB7 A1BG--HNF4A A1BG--ONECUT1 A1BG--PIK3CA
[11] A1BG--PIK3R1 A1BG--PRDX4 A1BG--PTPN11 A1BG--SETD7 A1BG--SMN1
[1... | Apache-2.0 | class02a_igraph_R.ipynb | curiositymap/Networks-in-Computational-Biology |
Do the same for the metabolic network: | pcdf_metab <- pcdf[pcdf$interaction_type %in% interaction_types_metab, c(1,3)]
graph_metab <- graph_from_data_frame(pcdf_metab,
directed=TRUE)
print(graph_metab) | IGRAPH 77472bf DN-- 7620 38145 --
+ attr: name (v/c)
+ edges from 77472bf (vertex names):
[1] A4GALT->CHEBI:17659 A4GALT->CHEBI:17950 A4GALT->CHEBI:18307
[4] A4GALT->CHEBI:18313 A4GALT->CHEBI:58223 A4GALT->CHEBI:67119
[7] A4GNT ->CHEBI:17659 A4GNT ->CHEBI:58223 AAAS ->CHEBI:1604
[10] AAAS ->CHEBI:2274 AACS ->C... | Apache-2.0 | class02a_igraph_R.ipynb | curiositymap/Networks-in-Computational-Biology |
Do the same for the directed protein-protein interactions: | pcdf_ppd <- pcdf[pcdf$interaction_type %in% interaction_types_ppd, c(1,3)]
graph_ppd <- graph_from_data_frame(pcdf_ppd,
directed=TRUE)
print(graph_ppd) | IGRAPH DN-- 16063 359713 --
+ attr: name (v/c), interaction_type (e/c)
IGRAPH DN-- 16063 359713 --
+ attr: name (v/c), interaction_type (e/c)
+ edges (vertex names):
[1] A1BG ->A2M A1BG ->AKT1 A1BG ->AKT1 A2M ->APOA1
[5] A2M ->CDC42 A2M ->RAC1 A2M ->RAC2 A2M ->RAC3
[9] A... | Apache-2.0 | class02a_igraph_R.ipynb | curiositymap/Networks-in-Computational-Biology |
Question: of the three networks that you just created, which has the most edges? Next, we need to create a small graph. Let's make a three-vertex undirected graph from an edge-list. Let's connect all vertices to all other vertices: 12, 23, 31. We'll once again use graph_from_data_farme to do this: | testgraph <- graph_from_data_frame(data.frame(c(1,2,3), c(2,3,1)), directed=FALSE) | _____no_output_____ | Apache-2.0 | class02a_igraph_R.ipynb | curiositymap/Networks-in-Computational-Biology |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.