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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Transforming and Creating Columns | df.assign(bmi=df['weight'] / (df['height']/100)**2)
df['bmi'] = df['weight'] / (df['height']/100)**2
df
df['something'] = [2,2,None,None,3]
df | _____no_output_____ | BSD-3-Clause | notebooks/03_Using_Pandas_Annotated.ipynb | abbarcenasj/bios-823-2019 |
Sorting Data Frames Sort on indexes | df.sort_index(axis=1)
df.sort_index(axis=0, ascending=False) | _____no_output_____ | BSD-3-Clause | notebooks/03_Using_Pandas_Annotated.ipynb | abbarcenasj/bios-823-2019 |
Sort on values | df.sort_values(by=['something', 'bmi'], ascending=[True, False]) | _____no_output_____ | BSD-3-Clause | notebooks/03_Using_Pandas_Annotated.ipynb | abbarcenasj/bios-823-2019 |
Summarizing Apply an aggregation function | df.select_dtypes(include=np.number)
df.select_dtypes(include=np.number).agg(np.sum)
df.agg(['count', np.sum, np.mean]) | _____no_output_____ | BSD-3-Clause | notebooks/03_Using_Pandas_Annotated.ipynb | abbarcenasj/bios-823-2019 |
Split-Apply-CombineWe often want to perform subgroup analysis (conditioning by some discrete or categorical variable). This is done with `groupby` followed by an aggregate function. Conceptually, we split the data frame into separate groups, apply the aggregate function to each group separately, then combine the aggre... | df['treatment'] = list('ababa')
df
grouped = df.groupby('treatment')
grouped.get_group('a')
grouped.mean() | _____no_output_____ | BSD-3-Clause | notebooks/03_Using_Pandas_Annotated.ipynb | abbarcenasj/bios-823-2019 |
Using `agg` with `groupby` | grouped.agg('mean')
grouped.agg(['mean', 'std'])
grouped.agg({'weight': ['mean', 'std'], 'height': ['min', 'max'], 'bmi': lambda x: (x**2).sum()}) | _____no_output_____ | BSD-3-Clause | notebooks/03_Using_Pandas_Annotated.ipynb | abbarcenasj/bios-823-2019 |
Using `trasnform` wtih `groupby` | g_mean = grouped['weight', 'height'].transform(np.mean)
g_mean
g_std = grouped['weight', 'height'].transform(np.std)
g_std
(df[['weight', 'height']] - g_mean)/g_std | _____no_output_____ | BSD-3-Clause | notebooks/03_Using_Pandas_Annotated.ipynb | abbarcenasj/bios-823-2019 |
Combining Data Frames | df
df1 = df.iloc[3:].copy()
df1.drop('something', axis=1, inplace=True)
df1 | _____no_output_____ | BSD-3-Clause | notebooks/03_Using_Pandas_Annotated.ipynb | abbarcenasj/bios-823-2019 |
Adding rowsNote that `pandas` aligns by column indexes automatically. | df.append(df1, sort=False)
pd.concat([df, df1], sort=False) | _____no_output_____ | BSD-3-Clause | notebooks/03_Using_Pandas_Annotated.ipynb | abbarcenasj/bios-823-2019 |
Adding columns | df.pid
df2 = pd.DataFrame(OrderedDict(pid=[649, 533, 400, 600], age=[23,34,45,56]))
df2.pid
df.pid = df.pid.astype('int')
pd.merge(df, df2, on='pid', how='inner')
pd.merge(df, df2, on='pid', how='left')
pd.merge(df, df2, on='pid', how='right')
pd.merge(df, df2, on='pid', how='outer') | _____no_output_____ | BSD-3-Clause | notebooks/03_Using_Pandas_Annotated.ipynb | abbarcenasj/bios-823-2019 |
Merging on the index | df1 = pd.DataFrame(dict(x=[1,2,3]), index=list('abc'))
df2 = pd.DataFrame(dict(y=[4,5,6]), index=list('abc'))
df3 = pd.DataFrame(dict(z=[7,8,9]), index=list('abc'))
df1
df2
df3
df1.join([df2, df3]) | _____no_output_____ | BSD-3-Clause | notebooks/03_Using_Pandas_Annotated.ipynb | abbarcenasj/bios-823-2019 |
Fixing common DataFrame issues Multiple variables in a column | df = pd.DataFrame(dict(pid_treat = ['A-1', 'B-2', 'C-1', 'D-2']))
df
df.pid_treat.str.split('-')
df.pid_treat.str.split('-').apply(pd.Series, index=['pid', 'treat']) | _____no_output_____ | BSD-3-Clause | notebooks/03_Using_Pandas_Annotated.ipynb | abbarcenasj/bios-823-2019 |
Multiple values in a cell | df = pd.DataFrame(dict(pid=['a', 'b', 'c'], vals = [(1,2,3), (4,5,6), (7,8,9)]))
df
df[['t1', 't2', 't3']] = df.vals.apply(pd.Series)
df
df.drop('vals', axis=1, inplace=True)
pd.melt(df, id_vars='pid', value_name='vals').drop('variable', axis=1) | _____no_output_____ | BSD-3-Clause | notebooks/03_Using_Pandas_Annotated.ipynb | abbarcenasj/bios-823-2019 |
Reshaping Data FramesSometimes we need to make rows into columns or vice versa. Converting multiple columns into a single columnThis is often useful if you need to condition on some variable. | url = 'https://raw.githubusercontent.com/uiuc-cse/data-fa14/gh-pages/data/iris.csv'
iris = pd.read_csv(url)
iris.head()
iris.shape
df_iris = pd.melt(iris, id_vars='species')
df_iris.sample(10) | _____no_output_____ | BSD-3-Clause | notebooks/03_Using_Pandas_Annotated.ipynb | abbarcenasj/bios-823-2019 |
Chaining commandsSometimes you see this functional style of method chaining that avoids the need for temporary intermediate variables. | (
iris.
sample(frac=0.2).
filter(regex='s.*').
assign(both=iris.sepal_length + iris.sepal_length).
groupby('species').agg(['mean', 'sum']).
pipe(lambda x: np.around(x, 1))
) | _____no_output_____ | BSD-3-Clause | notebooks/03_Using_Pandas_Annotated.ipynb | abbarcenasj/bios-823-2019 |
Moving between R and Python in Jupyter | %load_ext rpy2.ipython
import warnings
warnings.simplefilter('ignore', FutureWarning)
iris = %R iris
iris.head()
iris_py = iris.copy()
iris_py.Species = iris_py.Species.str.upper()
%%R -i iris_py -o iris_r
iris_r <- iris_py[1:3,]
iris_r | _____no_output_____ | BSD-3-Clause | notebooks/03_Using_Pandas_Annotated.ipynb | abbarcenasj/bios-823-2019 |
SLU13: Bias-Variance trade-off & Model Selection -- Examples--- 1. Model evaluation* a. [Train-test split](traintest)* b. [Train-val-test split](val)* c. [Cross validation](crossval) 2. [Learning curves](learningcurves) 1. Model evaluation | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import learning_curve
%matplotlib inline
# Create the DataFrame with the data
df = pd.read_csv("data/beer.csv")
# Cre... | Number of entries: 1000
| MIT | S01 - Bootcamp and Binary Classification/SLU13 - Bias-Variance tradeoff & Model Selection /Examples notebook.ipynb | FarhadManiCodes/batch5-students |
[Return to top](top) Create a training and a test set | from sklearn.model_selection import train_test_split
# Using 20 % of the data as test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
print("Number of training entries: ", X_train.shape[0])
print("Number of test entries: ", X_test.shape[0]) | Number of training entries: 800
Number of test entries: 200
| MIT | S01 - Bootcamp and Binary Classification/SLU13 - Bias-Variance tradeoff & Model Selection /Examples notebook.ipynb | FarhadManiCodes/batch5-students |
[Return to top](top) Create a training, test and validation set | # Using 20 % as test set and 20 % as validation set
X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4)
X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.50)
print("Number of training entries: ", X_train.shape[0])
print("Number of validation entries: ", X_val.shape[0])
pri... | Number of training entries: 600
Number of validation entries: 200
Number of test entries: 200
| MIT | S01 - Bootcamp and Binary Classification/SLU13 - Bias-Variance tradeoff & Model Selection /Examples notebook.ipynb | FarhadManiCodes/batch5-students |
[Return to top](top) Use cross-validation (using a given classifier) | from sklearn.model_selection import cross_val_score
knn = KNeighborsClassifier(n_neighbors=5)
# Use cv to specify the number of folds
scores = cross_val_score(knn, X, y, cv=5)
print(f"Mean of scores: {scores.mean():.3f}")
print(f"Variance of scores: {scores.var():.3f}") | Mean of scores: 0.916
Variance of scores: 0.000
| MIT | S01 - Bootcamp and Binary Classification/SLU13 - Bias-Variance tradeoff & Model Selection /Examples notebook.ipynb | FarhadManiCodes/batch5-students |
[Return to top](top) 2. Learning Curves Here is the function that is taken from the sklearn page on learning curves: | def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,
n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):
"""
Generate a simple plot of the test and training learning curve.
Parameters
----------
estimator : object type that implements the "fit" and "predict" metho... | _____no_output_____ | MIT | S01 - Bootcamp and Binary Classification/SLU13 - Bias-Variance tradeoff & Model Selection /Examples notebook.ipynb | FarhadManiCodes/batch5-students |
And remember the internals of what this function is actually doing by knowing how to use theoutput of the scikit [learning_curve](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.learning_curve.html) function | # here's where the magic happens! The learning curve function is going
# to take your classifier and your training data and subset the data
train_sizes, train_scores, test_scores = learning_curve(clf, X, y)
# 5 different training set sizes have been selected
# with the smallest being 59 and the largest being 594
# the... | _____no_output_____ | MIT | S01 - Bootcamp and Binary Classification/SLU13 - Bias-Variance tradeoff & Model Selection /Examples notebook.ipynb | FarhadManiCodes/batch5-students |
Phi_K advanced tutorialThis notebook guides you through the more advanced functionality of the phik package. This notebook will not cover all the underlying theory, but will just attempt to give an overview of all the options that are available. For a theoretical description the user is referred to our paper.The packa... | %%capture
# install phik (if not installed yet)
import sys
!"{sys.executable}" -m pip install phik
# import standard packages
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import itertools
import phik
from phik import resources
from phik.binning import bin_data
from phik.decorators import *
... | _____no_output_____ | Apache-2.0 | phik/notebooks/phik_tutorial_advanced.ipynb | ionicsolutions/PhiK |
Load dataA simulated dataset is part of the phik-package. The dataset concerns car insurance data. Load the dataset here: | data = pd.read_csv( resources.fixture('fake_insurance_data.csv.gz') )
data.head() | _____no_output_____ | Apache-2.0 | phik/notebooks/phik_tutorial_advanced.ipynb | ionicsolutions/PhiK |
Specify bin typesThe phik-package offers a way to calculate correlations between variables of mixed types. Variable types can be inferred automatically although we recommend to variable types to be specified by the user. Because interval type variables need to be binned in order to calculate phik and the significance,... | data_types = {'severity': 'interval',
'driver_age':'interval',
'satisfaction':'ordinal',
'mileage':'interval',
'car_size':'ordinal',
'car_use':'ordinal',
'car_color':'categorical',
'area':'categorical'}
interval_cols = [col for ... | _____no_output_____ | Apache-2.0 | phik/notebooks/phik_tutorial_advanced.ipynb | ionicsolutions/PhiK |
Phik correlation matrixNow let's start calculating the correlation phik between pairs of variables. Note that the original dataset is used as input, the binning of interval variables is done automatically. | phik_overview = data.phik_matrix(interval_cols=interval_cols)
phik_overview | _____no_output_____ | Apache-2.0 | phik/notebooks/phik_tutorial_advanced.ipynb | ionicsolutions/PhiK |
Specify binning per interval variableBinning can be set per interval variable individually. One can set the number of bins, or specify a list of bin edges. Note that the measured phik correlation is dependent on the chosen binning. The default binning is uniform between the min and max values of the interval variable. | bins = {'mileage':5, 'driver_age':[18,25,35,45,55,65,125]}
phik_overview = data.phik_matrix(interval_cols=interval_cols, bins=bins)
phik_overview | _____no_output_____ | Apache-2.0 | phik/notebooks/phik_tutorial_advanced.ipynb | ionicsolutions/PhiK |
Do not apply noise correctionFor low statistics samples often a correlation larger than zero is measured when no correlation is actually present in the true underlying distribution. This is not only the case for phik, but also for the pearson correlation and Cramer's phi (see figure 4 in XX ). In the phik calculation... | phik_overview = data.phik_matrix(interval_cols=interval_cols, noise_correction=False)
phik_overview | _____no_output_____ | Apache-2.0 | phik/notebooks/phik_tutorial_advanced.ipynb | ionicsolutions/PhiK |
Using a different expectation histogramBy default phik compares the 2d distribution of two (binned) variables with the distribution that assumes no dependency between them. One can also change the expected distribution though. Phi_K is calculated in the same way, but using the other expectation distribution. | from phik.binning import auto_bin_data
from phik.phik import phik_observed_vs_expected_from_rebinned_df, phik_from_hist2d
from phik.statistics import get_dependent_frequency_estimates
# get observed 2d histogram of two variables
cols = ["mileage", "car_size"]
icols = ["mileage"]
observed = data[cols].hist2d(interval_co... | _____no_output_____ | Apache-2.0 | phik/notebooks/phik_tutorial_advanced.ipynb | ionicsolutions/PhiK |
Statistical significance of the correlationWhen assessing correlations it is good practise to evaluate both the correlation and the significance of the correlation: a large correlation may be statistically insignificant, and vice versa a small correlation may be very significant. For instance, scipy.stats.pearsonr ret... | significance_overview = data.significance_matrix(interval_cols=interval_cols)
significance_overview | _____no_output_____ | Apache-2.0 | phik/notebooks/phik_tutorial_advanced.ipynb | ionicsolutions/PhiK |
Specify binning per interval variableBinning can be set per interval variable individually. One can set the number of bins, or specify a list of bin edges. Note that the measure phik correlation is dependent on the chosen binning. | bins = {'mileage':5, 'driver_age':[18,25,35,45,55,65,125]}
significance_overview = data.significance_matrix(interval_cols=interval_cols, bins=bins)
significance_overview | _____no_output_____ | Apache-2.0 | phik/notebooks/phik_tutorial_advanced.ipynb | ionicsolutions/PhiK |
Specify significance methodThe recommended method to calculate the significance of the correlation is a hybrid approach, which uses the G-test statistic. The number of degrees of freedom and an analytical, empirical description of the $\chi^2$ distribution are sed, based on Monte Carlo simulations. This method works w... | significance_overview = data.significance_matrix(interval_cols=interval_cols, significance_method='asymptotic')
significance_overview | _____no_output_____ | Apache-2.0 | phik/notebooks/phik_tutorial_advanced.ipynb | ionicsolutions/PhiK |
Simulation methodThe chi2 of a contingency table is measured using a comparison of the expected frequencies with the true frequencies in a contingency table. The expected frequencies can be simulated in a variety of ways. The following methods are implemented: - multinominal: Only the total number of records is fixed.... | # --- Warning, can be slow
# turned off here by default for unit testing purposes
#significance_overview = data.significance_matrix(interval_cols=interval_cols, simulation_method='hypergeometric')
#significance_overview | _____no_output_____ | Apache-2.0 | phik/notebooks/phik_tutorial_advanced.ipynb | ionicsolutions/PhiK |
Expected frequencies | from phik.simulation import sim_2d_data_patefield, sim_2d_product_multinominal, sim_2d_data
inputdata = data[['driver_age', 'area']].hist2d(interval_cols=['driver_age'])
inputdata | _____no_output_____ | Apache-2.0 | phik/notebooks/phik_tutorial_advanced.ipynb | ionicsolutions/PhiK |
Multinominal | simdata = sim_2d_data(inputdata.values)
print('data total:', inputdata.sum().sum())
print('sim total:', simdata.sum().sum())
print('data row totals:', inputdata.sum(axis=0).values)
print('sim row totals:', simdata.sum(axis=0))
print('data column totals:', inputdata.sum(axis=1).values)
print('sim column totals:', sim... | data total: 2000.0
sim total: 2000
data row totals: [ 65. 462. 724. 639. 110.]
sim row totals: [ 75 468 748 586 123]
data column totals: [388. 379. 388. 339. 281. 144. 56. 21. 2. 2.]
sim column totals: [378 380 375 335 281 164 59 25 1 2]
| Apache-2.0 | phik/notebooks/phik_tutorial_advanced.ipynb | ionicsolutions/PhiK |
product multinominal | simdata = sim_2d_product_multinominal(inputdata.values, axis=0)
print('data total:', inputdata.sum().sum())
print('sim total:', simdata.sum().sum())
print('data row totals:', inputdata.sum(axis=0).astype(int).values)
print('sim row totals:', simdata.sum(axis=0).astype(int))
print('data column totals:', inputdata.sum(... | data total: 2000.0
sim total: 2000
data row totals: [ 65 462 724 639 110]
sim row totals: [ 65 462 724 639 110]
data column totals: [388 379 388 339 281 144 56 21 2 2]
sim column totals: [399 353 415 349 272 139 45 22 4 2]
| Apache-2.0 | phik/notebooks/phik_tutorial_advanced.ipynb | ionicsolutions/PhiK |
hypergeometric ("patefield") | # patefield simulation needs compiled c++ code.
# only run this if the python binding to the (compiled) patefiled simulation function is found.
try:
from phik.simcore import _sim_2d_data_patefield
CPP_SUPPORT = True
except ImportError:
CPP_SUPPORT = False
if CPP_SUPPORT:
simdata = sim_2d_data_patefield... | data total: 2000.0
sim total: 2000
data row totals: [ 65 462 724 639 110]
sim row totals: [ 65 462 724 639 110]
data column totals: [388 379 388 339 281 144 56 21 2 2]
sim column totals: [388 379 388 339 281 144 56 21 2 2]
| Apache-2.0 | phik/notebooks/phik_tutorial_advanced.ipynb | ionicsolutions/PhiK |
Outlier significanceThe normal pearson correlation between two interval variables is easy to interpret. However, the phik correlation between two variables of mixed type is not always easy to interpret, especially when it concerns categorical variables. Therefore, functionality is provided to detect "outliers": excess... | c0 = 'mileage'
c1 = 'car_size'
tmp_interval_cols = ['mileage']
outlier_signifs, binning_dict = data[[c0,c1]].outlier_significance_matrix(interval_cols=tmp_interval_cols,
retbins=True)
outlier_signifs | _____no_output_____ | Apache-2.0 | phik/notebooks/phik_tutorial_advanced.ipynb | ionicsolutions/PhiK |
Specify binning per interval variableBinning can be set per interval variable individually. One can set the number of bins, or specify a list of bin edges. Note: in case a bin is created without any records this bin will be automatically dropped in the phik and (outlier) significance calculations. However, in the outl... | bins = [0,1E2, 1E3, 1E4, 1E5, 1E6]
outlier_signifs, binning_dict = data[[c0,c1]].outlier_significance_matrix(interval_cols=tmp_interval_cols,
bins=bins, retbins=True)
outlier_signifs | _____no_output_____ | Apache-2.0 | phik/notebooks/phik_tutorial_advanced.ipynb | ionicsolutions/PhiK |
Specify binning per interval variable -- dealing with underflow and overflowWhen specifying custom bins as situation can occur when the minimal (maximum) value in the data is smaller (larger) than the minimum (maximum) bin edge. Data points outside the specified range will be collected in the underflow (UF) and overfl... | bins = [1E2, 1E3, 1E4, 1E5]
outlier_signifs, binning_dict = data[[c0,c1]].outlier_significance_matrix(interval_cols=tmp_interval_cols,
bins=bins, retbins=True,
drop_under... | _____no_output_____ | Apache-2.0 | phik/notebooks/phik_tutorial_advanced.ipynb | ionicsolutions/PhiK |
Dealing with NaN's in the data Let's add some missing values to our data | data.loc[np.random.choice(range(len(data)), size=10), 'car_size'] = np.nan
data.loc[np.random.choice(range(len(data)), size=10), 'mileage'] = np.nan | _____no_output_____ | Apache-2.0 | phik/notebooks/phik_tutorial_advanced.ipynb | ionicsolutions/PhiK |
Sometimes there can be information in the missing values and in which case you might want to consider the NaN values as a separate category. This can be achieved by setting the dropna argument to False. | bins = [1E2, 1E3, 1E4, 1E5]
outlier_signifs, binning_dict = data[[c0,c1]].outlier_significance_matrix(interval_cols=tmp_interval_cols,
bins=bins, retbins=True,
drop_under... | _____no_output_____ | Apache-2.0 | phik/notebooks/phik_tutorial_advanced.ipynb | ionicsolutions/PhiK |
Here OF and UF are the underflow and overflow bin of car_size, respectively.To just ignore records with missing values set dropna to True (default). | bins = [1E2, 1E3, 1E4, 1E5]
outlier_signifs, binning_dict = data[[c0,c1]].outlier_significance_matrix(interval_cols=tmp_interval_cols,
bins=bins, retbins=True,
drop_under... | _____no_output_____ | Apache-2.0 | phik/notebooks/phik_tutorial_advanced.ipynb | ionicsolutions/PhiK |
Support Vector Clustering visualizedTo get started, please click on the cell with the code below and hit `Shift + Enter` This may take a while. Support Vector Clustering(SVC) is a variation of Support Vector Machine (SVM).SVC is a way of determining a boudary point between different labels. It utilizes a kernel metho... | %matplotlib notebook
import matplotlib.pyplot as plt
import numpy as np
from ipywidgets import *
from IPython.display import display
from sklearn.svm import SVC
plt.style.use('ggplot')
def plot_data(data, labels, sep):
data_x = data[:, 0]
data_y = data[:, 1]
sep_x = sep[:, 0]
sep_y = sep[:, 1]
# ... | _____no_output_____ | MIT | demo/classification.ipynb | DandikUnited/dandikunited.github.io |
import pandas as pd
import numpy as np
values_1 = np.random.randint(10, size=10)
values_2 = np.random.randint(10, size = 10)
print(values_1)
print(values_2)
years = np.arange(2010, 2020)
print(years)
groups = ['A','A','B','A','B','B','C','A','C','C']
len(groups)
df = pd.DataFrame({'group':groups, 'year':years,'value_1'... | _____no_output_____ | MIT | 2020_07_15_pandas_functions.ipynb | daekee0325/Data-Analysis | |
SMPLE | Sample1 = df.sample(n=3)
Sample1
Sample2 = df.sample(frac=0.5)
Sample2
df['new_col'].where(df['new_col']>0,0)
np.where(df['new_col'] >0, df['new_col'], 0)
| _____no_output_____ | MIT | 2020_07_15_pandas_functions.ipynb | daekee0325/Data-Analysis |
isin | years = ['2010','2014','2015']
df[df.year.isin(years)]
df.loc[:2, ['group','year'] ]
df.loc[[1,3,5],['year','value_1']]
df['value_1']
df.value_1.pct_change()
df.value_1.sort_values()
df.value_1.sort_values().pct_change()
df['rank_1'] = df['value_1'].rank( )
df
df.select_dtypes(exclude='int64')
df.replace({'A':'A_1','B'... | _____no_output_____ | MIT | 2020_07_15_pandas_functions.ipynb | daekee0325/Data-Analysis |
Description This notebook runs some pre-analyses using DBSCAN to explore the best set of parameters (`min_samples` and `eps`) to cluster `pca` data version. Environment variables | from IPython.display import display
import conf
N_JOBS = conf.GENERAL["N_JOBS"]
display(N_JOBS)
%env MKL_NUM_THREADS=$N_JOBS
%env OPEN_BLAS_NUM_THREADS=$N_JOBS
%env NUMEXPR_NUM_THREADS=$N_JOBS
%env OMP_NUM_THREADS=$N_JOBS | env: MKL_NUM_THREADS=2
env: OPEN_BLAS_NUM_THREADS=2
env: NUMEXPR_NUM_THREADS=2
env: OMP_NUM_THREADS=2
| BSD-2-Clause-Patent | nbs/12_cluster_analysis/pre_analysis/06_02-dbscan-pca.ipynb | greenelab/phenoplier |
Modules loading | %load_ext autoreload
%autoreload 2
from pathlib import Path
import numpy as np
import pandas as pd
from sklearn.neighbors import NearestNeighbors
from sklearn.metrics import pairwise_distances
from sklearn.cluster import DBSCAN
from sklearn.metrics import (
silhouette_score,
calinski_harabasz_score,
davies... | _____no_output_____ | BSD-2-Clause-Patent | nbs/12_cluster_analysis/pre_analysis/06_02-dbscan-pca.ipynb | greenelab/phenoplier |
Global settings | np.random.seed(0)
CLUSTERING_ATTRIBUTES_TO_SAVE = ["n_clusters"] | _____no_output_____ | BSD-2-Clause-Patent | nbs/12_cluster_analysis/pre_analysis/06_02-dbscan-pca.ipynb | greenelab/phenoplier |
Data version: pca | INPUT_SUBSET = "pca"
INPUT_STEM = "z_score_std-projection-smultixcan-efo_partial-mashr-zscores"
DR_OPTIONS = {
"n_components": 50,
"svd_solver": "full",
"random_state": 0,
}
input_filepath = Path(
conf.RESULTS["DATA_TRANSFORMATIONS_DIR"],
INPUT_SUBSET,
generate_result_set_name(
DR_OPTION... | _____no_output_____ | BSD-2-Clause-Patent | nbs/12_cluster_analysis/pre_analysis/06_02-dbscan-pca.ipynb | greenelab/phenoplier |
Tests different k values (k-NN) | # `k_values` is the full range of k for kNN, whereas `k_values_to_explore` is a
# subset that will be explored in this notebook. If the analysis works, then
# `k_values` and `eps_range_per_k` below are copied to the notebook that will
# produce the final DBSCAN runs (`../002_[...]-dbscan-....ipynb`)
k_values = np.arang... | _____no_output_____ | BSD-2-Clause-Patent | nbs/12_cluster_analysis/pre_analysis/06_02-dbscan-pca.ipynb | greenelab/phenoplier |
Extended test Generate clusterers | CLUSTERING_OPTIONS = {}
# K_RANGE is the min_samples parameter in DBSCAN (sklearn)
CLUSTERING_OPTIONS["K_RANGE"] = k_values_to_explore
CLUSTERING_OPTIONS["EPS_RANGE_PER_K"] = eps_range_per_k_to_explore
CLUSTERING_OPTIONS["EPS_STEP"] = 33
CLUSTERING_OPTIONS["METRIC"] = "euclidean"
display(CLUSTERING_OPTIONS)
CLUSTERER... | _____no_output_____ | BSD-2-Clause-Patent | nbs/12_cluster_analysis/pre_analysis/06_02-dbscan-pca.ipynb | greenelab/phenoplier |
Generate ensemble | data_dist = pairwise_distances(data, metric=CLUSTERING_OPTIONS["METRIC"])
data_dist.shape
pd.Series(data_dist.flatten()).describe().apply(str)
ensemble = generate_ensemble(
data_dist,
CLUSTERERS,
attributes=CLUSTERING_ATTRIBUTES_TO_SAVE,
)
ensemble.shape
ensemble.head()
_tmp = ensemble["n_clusters"].value_c... | _____no_output_____ | BSD-2-Clause-Patent | nbs/12_cluster_analysis/pre_analysis/06_02-dbscan-pca.ipynb | greenelab/phenoplier |
Testing | assert ensemble_stats["min"] > 1
assert not ensemble["n_clusters"].isna().any()
# all partitions have the right size
assert np.all(
[part["partition"].shape[0] == data.shape[0] for idx, part in ensemble.iterrows()]
) | _____no_output_____ | BSD-2-Clause-Patent | nbs/12_cluster_analysis/pre_analysis/06_02-dbscan-pca.ipynb | greenelab/phenoplier |
Add clustering quality measures | def _remove_nans(data, part):
not_nan_idx = ~np.isnan(part)
return data.iloc[not_nan_idx], part[not_nan_idx]
def _apply_func(func, data, part):
no_nan_data, no_nan_part = _remove_nans(data, part)
return func(no_nan_data, no_nan_part)
ensemble = ensemble.assign(
si_score=ensemble["partition"].apply... | _____no_output_____ | BSD-2-Clause-Patent | nbs/12_cluster_analysis/pre_analysis/06_02-dbscan-pca.ipynb | greenelab/phenoplier |
Cluster quality | with pd.option_context("display.max_rows", None, "display.max_columns", None):
_df = ensemble.groupby(["n_clusters"]).mean()
display(_df)
with sns.plotting_context("talk", font_scale=0.75), sns.axes_style(
"whitegrid", {"grid.linestyle": "--"}
):
fig = plt.figure(figsize=(14, 6))
ax = sns.pointplot(... | _____no_output_____ | BSD-2-Clause-Patent | nbs/12_cluster_analysis/pre_analysis/06_02-dbscan-pca.ipynb | greenelab/phenoplier |
Generic startnotebook, course on webscraping*By Olav ten Bosch, Dick Windmeijer and Marijn Detiger* Documentation: [Requests.py](http://docs.python-requests.org) [Beautifulsoup.py](https://www.crummy.com/software/BeautifulSoup/bs4/doc/) | # Imports:
import requests
from bs4 import BeautifulSoup
import time # for sleeping between multiple requests
#Issue a request:
#r1 = requests.get('http://testing-ground.scraping.pro')
#print(r1.status_code, r1.headers['content-type'], r1.encoding, r1.text)
#Issue a request with dedicated user-ag... | _____no_output_____ | CC-BY-4.0 | 20200907/GenericStartNotebook.ipynb | SNStatComp/CBSAcademyBD |
Multi Investment OptimizationIn the following, we show how PyPSA can deal with multi-investment optimization, also known as multi-horizon optimization. Here, the total set of snapshots is divided into investment periods. For the model, this translates into multi-indexed snapshots with the first level being the investm... | import pypsa
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt | _____no_output_____ | MIT | examples/notebooks/multi-investment-optimisation.ipynb | p-glaum/PyPSA |
We set up the network with investment periods and snapshots. | n = pypsa.Network()
years = [2020, 2030, 2040, 2050]
freq = "24"
snapshots = pd.DatetimeIndex([])
for year in years:
period = pd.date_range(
start="{}-01-01 00:00".format(year),
freq="{}H".format(freq),
periods=8760 / float(freq),
)
snapshots = snapshots.append(period)
# convert to... | _____no_output_____ | MIT | examples/notebooks/multi-investment-optimisation.ipynb | p-glaum/PyPSA |
Set the years and objective weighting per investment period. For the objective weighting, we consider a discount rate defined by $$ D(t) = \dfrac{1}{(1+r)^t} $$ where $r$ is the discount rate. For each period we sum up all discounts rates of the corresponding years which gives us the effective objective weighting. | n.investment_period_weightings["years"] = list(np.diff(years)) + [10]
r = 0.01
T = 0
for period, nyears in n.investment_period_weightings.years.items():
discounts = [(1 / (1 + r) ** t) for t in range(T, T + nyears)]
n.investment_period_weightings.at[period, "objective"] = sum(discounts)
T += nyears
n.inves... | _____no_output_____ | MIT | examples/notebooks/multi-investment-optimisation.ipynb | p-glaum/PyPSA |
Add the components | for i in range(3):
n.add("Bus", "bus {}".format(i))
# add three lines in a ring
n.add(
"Line",
"line 0->1",
bus0="bus 0",
bus1="bus 1",
)
n.add(
"Line",
"line 1->2",
bus0="bus 1",
bus1="bus 2",
capital_cost=10,
build_year=2030,
)
n.add(
"Line",
"line 2->0",
bus... | _____no_output_____ | MIT | examples/notebooks/multi-investment-optimisation.ipynb | p-glaum/PyPSA |
Add the load | load_var = pd.Series(
100 * np.random.rand(len(n.snapshots)), index=n.snapshots, name="load"
)
n.add("Load", "load 2", bus="bus 2", p_set=load_var)
load_fix = pd.Series(75, index=n.snapshots, name="load")
n.add("Load", "load 1", bus="bus 1", p_set=load_fix) | _____no_output_____ | MIT | examples/notebooks/multi-investment-optimisation.ipynb | p-glaum/PyPSA |
Run the optimization | n.loads_t.p_set
n.lopf(pyomo=False, multi_investment_periods=True)
c = "Generator"
df = pd.concat(
{
period: n.get_active_assets(c, period) * n.df(c).p_nom_opt
for period in n.investment_periods
},
axis=1,
)
df.T.plot.bar(
stacked=True,
edgecolor="white",
width=1,
ylabel="Cap... | _____no_output_____ | MIT | examples/notebooks/multi-investment-optimisation.ipynb | p-glaum/PyPSA |
Intro**This is Lesson 3 in the [Deep Learning](https://www.kaggle.com/education/machine-learning) track** At the end of this lesson, you will be able to write TensorFlow and Keras code to use one of the best models in computer vision. Lesson | from IPython.display import YouTubeVideo
YouTubeVideo('sDG5tPtsbSA', width=800, height=450) | _____no_output_____ | MIT | deep learning and computer vision/kaggle-learn-tensorflow-exercise.ipynb | DrDarbin/Kaggle-learn-tasks-solutions |
Sample Code Choose Images to Work With | from os.path import join
image_dir = '../input/dog-breed-identification/train/'
img_paths = [join(image_dir, filename) for filename in
['0246f44bb123ce3f91c939861eb97fb7.jpg',
'84728e78632c0910a69d33f82e62638c.jpg',
'8825e914555803f4c6... | _____no_output_____ | MIT | deep learning and computer vision/kaggle-learn-tensorflow-exercise.ipynb | DrDarbin/Kaggle-learn-tasks-solutions |
Function to Read and Prep Images for Modeling | import numpy as np
from tensorflow.python.keras.applications.resnet50 import preprocess_input
from tensorflow.python.keras.preprocessing.image import load_img, img_to_array
image_size = 224
def read_and_prep_images(img_paths, img_height=image_size, img_width=image_size):
imgs = [load_img(img_path, target_size=(im... | _____no_output_____ | MIT | deep learning and computer vision/kaggle-learn-tensorflow-exercise.ipynb | DrDarbin/Kaggle-learn-tasks-solutions |
Create Model with Pre-Trained Weights File. Make Predictions | from tensorflow.python.keras.applications import ResNet50
my_model = ResNet50(weights='../input/resnet50/resnet50_weights_tf_dim_ordering_tf_kernels.h5')
test_data = read_and_prep_images(img_paths)
preds = my_model.predict(test_data) | _____no_output_____ | MIT | deep learning and computer vision/kaggle-learn-tensorflow-exercise.ipynb | DrDarbin/Kaggle-learn-tasks-solutions |
Visualize Predictions | from learntools.deep_learning.decode_predictions import decode_predictions
from IPython.display import Image, display
most_likely_labels = decode_predictions(preds, top=3, class_list_path='../input/resnet50/imagenet_class_index.json')
for i, img_path in enumerate(img_paths):
display(Image(img_path))
print(mos... | _____no_output_____ | MIT | deep learning and computer vision/kaggle-learn-tensorflow-exercise.ipynb | DrDarbin/Kaggle-learn-tasks-solutions |
Rhetorical relations classification used in tree building: ESIMPrepare data and model-related scripts.Evaluate models.Make and evaluate ansembles for ESIM and BiMPM model / ESIM and feature-based model.Output: - ``models/relation_predictor_esim/*`` | %load_ext autoreload
%autoreload 2
import os
import glob
import pandas as pd
import numpy as np
import pickle
from utils.file_reading import read_edus, read_gold, read_negative, read_annotation | _____no_output_____ | MIT | src/maintenance/3.5_relations_classification_esim.ipynb | tchewik/isanlp_rst |
Make a directory | MODEL_PATH = 'models/label_predictor_esim'
! mkdir $MODEL_PATH
TRAIN_FILE_PATH = os.path.join(MODEL_PATH, 'nlabel_cf_train.tsv')
DEV_FILE_PATH = os.path.join(MODEL_PATH, 'nlabel_cf_dev.tsv')
TEST_FILE_PATH = os.path.join(MODEL_PATH, 'nlabel_cf_test.tsv') | mkdir: cannot create directory ‘models/label_predictor_esim’: File exists
| MIT | src/maintenance/3.5_relations_classification_esim.ipynb | tchewik/isanlp_rst |
Prepare train/test sets | IN_PATH = 'data_labeling'
train_samples = pd.read_pickle(os.path.join(IN_PATH, 'train_samples.pkl'))
dev_samples = pd.read_pickle(os.path.join(IN_PATH, 'dev_samples.pkl'))
test_samples = pd.read_pickle(os.path.join(IN_PATH, 'test_samples.pkl'))
counts = train_samples['relation'].value_counts(normalize=False).values
NU... | _____no_output_____ | MIT | src/maintenance/3.5_relations_classification_esim.ipynb | tchewik/isanlp_rst |
Modify model (Add F1, concatenated encoding) | %%writefile models/bimpm_custom_package/model/esim.py
from typing import Dict, List, Any, Optional
import numpy
import torch
from allennlp.common.checks import check_dimensions_match
from allennlp.data import TextFieldTensors, Vocabulary
from allennlp.models.model import Model
from allennlp.modules import FeedForwar... | _____no_output_____ | MIT | src/maintenance/3.5_relations_classification_esim.ipynb | tchewik/isanlp_rst |
2. Generate config files ELMo | %%writefile $MODEL_PATH/config_elmo.json
local NUM_EPOCHS = 200;
local LR = 1e-3;
local LSTM_ENCODER_HIDDEN = 25;
{
"dataset_reader": {
"type": "quora_paraphrase",
"tokenizer": {
"type": "just_spaces"
},
"token_indexers": {
"token_characters": {
"type": "characters",
"min... | _____no_output_____ | MIT | src/maintenance/3.5_relations_classification_esim.ipynb | tchewik/isanlp_rst |
3. Scripts for training/prediction Option 1. Directly from the config Train a model | %%writefile models/train_label_predictor_esim.sh
# usage:
# $ cd models
# $ sh train_label_predictor.sh {bert|elmo} result_30
export METHOD=${1}
export RESULT_DIR=${2}
export DEV_FILE_PATH="nlabel_cf_dev.tsv"
export TEST_FILE_PATH="nlabel_cf_test.tsv"
rm -r label_predictor_esim/${RESULT_DIR}/
allennlp train -s label... | _____no_output_____ | MIT | src/maintenance/3.5_relations_classification_esim.ipynb | tchewik/isanlp_rst |
Predict on dev&test | %%writefile models/eval_label_predictor_esim.sh
# usage:
# $ cd models
# $ sh train_label_predictor.sh {bert|elmo} result_30
export METHOD=${1}
export RESULT_DIR=${2}
export DEV_FILE_PATH="nlabel_cf_dev.tsv"
export TEST_FILE_PATH="nlabel_cf_test.tsv"
allennlp predict --use-dataset-reader --silent \
--output-file... | _____no_output_____ | MIT | src/maintenance/3.5_relations_classification_esim.ipynb | tchewik/isanlp_rst |
(optional) predict on train | %%writefile models/eval_label_predictor_train.sh
# usage:
# $ cd models
# $ sh eval_label_predictor_train.sh {bert|elmo} result_30
export METHOD=${1}
export RESULT_DIR=${2}
export TEST_FILE_PATH="nlabel_cf_train.tsv"
allennlp predict --use-dataset-reader --silent \
--output-file label_predictor_bimpm/${RESULT_DI... | _____no_output_____ | MIT | src/maintenance/3.5_relations_classification_esim.ipynb | tchewik/isanlp_rst |
Option 2. Using wandb for parameters adjustment | %%writefile ../../../maintenance_rst/models/wandb_label_predictor_esim.yaml
name: label_predictor_esim
program: wandb_allennlp # this is a wrapper console script around allennlp commands. It is part of wandb-allennlp
method: bayes
## Do not for get to use the command keyword to specify the following command structure
... | _____no_output_____ | MIT | src/maintenance/3.5_relations_classification_esim.ipynb | tchewik/isanlp_rst |
3. Run training ``wandb sweep wandb_label_predictor_esim.yaml``(returns %sweepname1)``wandb sweep wandb_label_predictor2.yaml``(returns %sweepname2)``wandb agent --count 1 %sweepname1 && wandb agent --count 1 %sweepname2`` Move the best model in label_predictor_bimpm | ! ls -laht models/wandb
! cp -r models/wandb/run-20201218_123424-kcphaqhi/training_dumps models/label_predictor_esim/esim_elmo | _____no_output_____ | MIT | src/maintenance/3.5_relations_classification_esim.ipynb | tchewik/isanlp_rst |
**Or** load from wandb by %sweepname | import wandb
api = wandb.Api()
run = api.run("tchewik/tmp/7hum4oom")
for file in run.files():
file.download(replace=True)
! cp -r training_dumps models/label_predictor_bimpm/toasty-sweep-1 | _____no_output_____ | MIT | src/maintenance/3.5_relations_classification_esim.ipynb | tchewik/isanlp_rst |
And run evaluation from shell``sh eval_label_predictor_esim.sh {elmo|elmo_fasttext} toasty-sweep-1`` 4. Evaluate classifier | def load_predictions(path):
result = []
vocab = []
with open(path, 'r') as file:
for line in file.readlines():
line = json.loads(line)
if line.get("label"):
result.append(line.get("label"))
elif line.get("label_probs"):
if not ... | _____no_output_____ | MIT | src/maintenance/3.5_relations_classification_esim.ipynb | tchewik/isanlp_rst |
On dev set | import pandas as pd
import json
true = pd.read_csv(DEV_FILE_PATH, sep='\t', header=None)[0].values.tolist()
pred = load_predictions(f'{MODEL_PATH}/{RESULT_DIR}/predictions_dev.json')
from sklearn.metrics import classification_report
print(classification_report(true[:len(pred)], pred, digits=4))
test_metrics = classif... | _____no_output_____ | MIT | src/maintenance/3.5_relations_classification_esim.ipynb | tchewik/isanlp_rst |
On train set (optional) | import pandas as pd
import json
true = pd.read_csv('models/label_predictor_bimpm/nlabel_cf_train.tsv', sep='\t', header=None)[0].values.tolist()
pred = load_predictions(f'{MODEL_PATH}/{RESULT_DIR}/predictions_train.json')
print(classification_report(true[:len(pred)], pred, digits=4))
file = 'models/label_predictor_ls... | _____no_output_____ | MIT | src/maintenance/3.5_relations_classification_esim.ipynb | tchewik/isanlp_rst |
On test set | import pandas as pd
import json
true = pd.read_csv(TEST_FILE_PATH, sep='\t', header=None)[0].values.tolist()
pred = load_predictions(f'{MODEL_PATH}/{RESULT_DIR}/predictions_test.json')
print(classification_report(true[:len(pred)], pred, digits=4))
test_metrics = classification_report(true[:len(pred)], pred, digits=4,... | _____no_output_____ | MIT | src/maintenance/3.5_relations_classification_esim.ipynb | tchewik/isanlp_rst |
Ensemble: (Logreg+Catboost) + ESIM | ! ls models/label_predictor_esim
import json
model_vocab = open(MODEL_PATH + '/' + RESULT_DIR + '/vocabulary/labels.txt', 'r').readlines()
model_vocab = [label.strip() for label in model_vocab]
catboost_vocab = [
'attribution_NS', 'attribution_SN', 'background_NS',
'cause-effect_NS', 'cause-effect_SN', 'compari... | /opt/.pyenv/versions/3.7.4/lib/python3.7/site-packages/sklearn/base.py:318: UserWarning: Trying to unpickle estimator Pipeline from version 0.22.2.post1 when using version 0.22.1. This might lead to breaking code or invalid results. Use at your own risk.
UserWarning)
/opt/.pyenv/versions/3.7.4/lib/python3.7/site-pack... | MIT | src/maintenance/3.5_relations_classification_esim.ipynb | tchewik/isanlp_rst |
On dev set | from sklearn import metrics
TARGET = 'relation'
y_dev, X_dev = dev_samples['relation'].to_frame(), dev_samples.drop('relation', axis=1).drop(
columns=drop_columns + ['category_id', 'index'])
X_scaled_np = scaler.transform(X_dev)
X_dev = pd.DataFrame(X_scaled_np, index=X_dev.index)
catboost_predictions = load_s... | weighted f1: 0.5413872373769657
macro f1: 0.5354738926873194
accuracy: 0.5389321468298109
precision recall f1-score support
attribution_NS 0.8409 0.9024 0.8706 82
attribution_SN 0.8424 0.8564 0.8493 181
... | MIT | src/maintenance/3.5_relations_classification_esim.ipynb | tchewik/isanlp_rst |
On test set | _test_samples = test_samples[:]
test_samples = _test_samples[:]
mask = test_samples.filename.str.contains('news')
test_samples = test_samples[test_samples['filename'].str.contains('news')]
mask.shape
test_samples.shape
def mask_predictions(predictions, mask):
result = []
mask = mask.values
for i, prediction... | _____no_output_____ | MIT | src/maintenance/3.5_relations_classification_esim.ipynb | tchewik/isanlp_rst |
Ensemble: BiMPM + ESIM On dev set | !ls models/label_predictor_bimpm/
from sklearn import metrics
TARGET = 'relation'
y_dev, X_dev = dev_samples['relation'].to_frame(), dev_samples.drop('relation', axis=1).drop(
columns=drop_columns + ['category_id', 'index'])
X_scaled_np = scaler.transform(X_dev)
X_dev = pd.DataFrame(X_scaled_np, index=X_dev.ind... | _____no_output_____ | MIT | src/maintenance/3.5_relations_classification_esim.ipynb | tchewik/isanlp_rst |
On test set | TARGET = 'relation'
y_test, X_test = test_samples[TARGET].to_frame(), test_samples.drop(TARGET, axis=1).drop(
columns=drop_columns + ['category_id', 'index'])
X_scaled_np = scaler.transform(X_test)
X_test = pd.DataFrame(X_scaled_np, index=X_test.index)
bimpm = load_neural_predictions(f'models/label_predictor_bim... | _____no_output_____ | MIT | src/maintenance/3.5_relations_classification_esim.ipynb | tchewik/isanlp_rst |
Quantitative Value Strategy"Value investing" means investing in the stocks that are cheapest relative to common measures of business value (like earnings or assets).For this project, we're going to build an investing strategy that selects the 50 stocks with the best value metrics. From there, we will calculate recomme... | import numpy as np
import pandas as pd
import xlsxwriter
import requests
from scipy import stats
import math | _____no_output_____ | MIT | 003_quantitative_value_strategy.ipynb | gyalpodongo/algorithmic_trading_python |
Importing Our List of Stocks & API TokenAs before, we'll need to import our list of stocks and our API token before proceeding. Make sure the .csv file is still in your working directory and import it with the following command: | stocks = pd.read_csv('sp_500_stocks.csv')
from secrets import IEX_CLOUD_API_TOKEN | _____no_output_____ | MIT | 003_quantitative_value_strategy.ipynb | gyalpodongo/algorithmic_trading_python |
Making Our First API CallIt's now time to make the first version of our value screener!We'll start by building a simple value screener that ranks securities based on a single metric (the price-to-earnings ratio). | symbol = 'aapl'
api_url = f"https://sandbox.iexapis.com/stable/stock/{symbol}/quote?token={IEX_CLOUD_API_TOKEN}"
data = requests.get(api_url).json() | _____no_output_____ | MIT | 003_quantitative_value_strategy.ipynb | gyalpodongo/algorithmic_trading_python |
Parsing Our API CallThis API call has the metric we need - the price-to-earnings ratio.Here is an example of how to parse the metric from our API call: | price = data['latestPrice']
pe_ratio = data['peRatio']
pe_ratio | _____no_output_____ | MIT | 003_quantitative_value_strategy.ipynb | gyalpodongo/algorithmic_trading_python |
Executing A Batch API Call & Building Our DataFrameJust like in our first project, it's now time to execute several batch API calls and add the information we need to our DataFrame.We'll start by running the following code cell, which contains some code we already built last time that we can re-use for this project. M... | # Function sourced from
# https://stackoverflow.com/questions/312443/how-do-you-split-a-list-into-evenly-sized-chunks
def chunks(lst, n):
"""Yield successive n-sized chunks from lst."""
for i in range(0, len(lst), n):
yield lst[i:i + n]
symbol_groups = list(chunks(stocks['Ticker'], 100))
sy... | _____no_output_____ | MIT | 003_quantitative_value_strategy.ipynb | gyalpodongo/algorithmic_trading_python |
Now we need to create a blank DataFrame and add our data to the data frame one-by-one. | df = pd.DataFrame(columns = my_columns)
for batch in symbol_strings:
batch_api_call_url = f"https://sandbox.iexapis.com/stable/stock/market/batch?symbols={batch}&types=quote&token={IEX_CLOUD_API_TOKEN}"
data = requests.get(batch_api_call_url).json()
for symbol in batch.split(','):
df = df.append(
... | _____no_output_____ | MIT | 003_quantitative_value_strategy.ipynb | gyalpodongo/algorithmic_trading_python |
Removing Glamour StocksThe opposite of a "value stock" is a "glamour stock". Since the goal of this strategy is to identify the 50 best value stocks from our universe, our next step is to remove glamour stocks from the DataFrame.We'll sort the DataFrame by the stocks' price-to-earnings ratio, and drop all stocks outsi... | df.sort_values('Price-to-Earnings Ratio', ascending=False, inplace=True)
df = df[df['Price-to-Earnings Ratio'] > 0]
df = df[:50]
df.reset_index(inplace=True, drop=True)
df | /var/folders/q_/gmxdkf893w3bm9wxvh6635t80000gp/T/ipykernel_89390/1321168316.py:1: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
df.sor... | MIT | 003_quantitative_value_strategy.ipynb | gyalpodongo/algorithmic_trading_python |
Calculating the Number of Shares to BuyWe now need to calculate the number of shares we need to buy. To do this, we will use the `portfolio_input` function that we created in our momentum project.I have included this function below. | def portfolio_input():
global portfolio_size
portfolio_size = input("Enter the value of your portfolio:")
try:
portfolio_size = float(portfolio_size)
except ValueError:
print("That's not a number! \n Try again:")
portfolio_size = input("Enter the value of your portfolio:") | _____no_output_____ | MIT | 003_quantitative_value_strategy.ipynb | gyalpodongo/algorithmic_trading_python |
Use the `portfolio_input` function to accept a `portfolio_size` variable from the user of this script. | portfolio_input() | _____no_output_____ | MIT | 003_quantitative_value_strategy.ipynb | gyalpodongo/algorithmic_trading_python |
You can now use the global `portfolio_size` variable to calculate the number of shares that our strategy should purchase. | position_size = portfolio_size/len(df.index)
for row in df.index:
df.loc[row, 'Number of Shares to Buy'] = math.floor(position_size/df.loc[row, 'Price'])
df | _____no_output_____ | MIT | 003_quantitative_value_strategy.ipynb | gyalpodongo/algorithmic_trading_python |
Building a Better (and More Realistic) Value StrategyEvery valuation metric has certain flaws.For example, the price-to-earnings ratio doesn't work well with stocks with negative earnings.Similarly, stocks that buyback their own shares are difficult to value using the price-to-book ratio.Investors typically use a `com... | symbol = 'AAPL'
batch_api_call_url = f"https://sandbox.iexapis.com/stable/stock/market/batch?symbols={symbol}&types=quote,advanced-stats&token={IEX_CLOUD_API_TOKEN}"
data = requests.get(batch_api_call_url).json()
# * Price-to-earnings ratio
pe_ratio = data[symbol]['quote']['peRatio']
# * Price-to-book ratio
pb_ratio = ... | _____no_output_____ | MIT | 003_quantitative_value_strategy.ipynb | gyalpodongo/algorithmic_trading_python |
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