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 |
|---|---|---|---|---|---|
Step 6 (again) - Deploy the model for the web appNow that we know that our model is working, it's time to create some custom inference code so that we can send the model a review which has not been processed and have it determine the sentiment of the review.As we saw above, by default the estimator which we created, w... | !pygmentize serve/predict.py | [34mimport[39;49;00m [04m[36margparse[39;49;00m
[34mimport[39;49;00m [04m[36mjson[39;49;00m
[34mimport[39;49;00m [04m[36mos[39;49;00m
[34mimport[39;49;00m [04m[36mpickle[39;49;00m
[34mimport[39;49;00m [04m[36msys[39;49;00m
[34mimport[39;49;00m [04m[36msagemaker_containers[39;49;00m
... | MIT | Project/SageMaker Project.ipynb | csuquanyanfei/ML_Sagemaker_Studies_Project1 |
As mentioned earlier, the `model_fn` method is the same as the one provided in the training code and the `input_fn` and `output_fn` methods are very simple and your task will be to complete the `predict_fn` method. Make sure that you save the completed file as `predict.py` in the `serve` directory.**TODO**: Complete th... | from sagemaker.predictor import RealTimePredictor
from sagemaker.pytorch import PyTorchModel
class StringPredictor(RealTimePredictor):
def __init__(self, endpoint_name, sagemaker_session):
super(StringPredictor, self).__init__(endpoint_name, sagemaker_session, content_type='text/plain')
model = PyTorchMod... | Parameter image will be renamed to image_uri in SageMaker Python SDK v2.
'create_image_uri' will be deprecated in favor of 'ImageURIProvider' class in SageMaker Python SDK v2.
| MIT | Project/SageMaker Project.ipynb | csuquanyanfei/ML_Sagemaker_Studies_Project1 |
Testing the modelNow that we have deployed our model with the custom inference code, we should test to see if everything is working. Here we test our model by loading the first `250` positive and negative reviews and send them to the endpoint, then collect the results. The reason for only sending some of the data is t... | import glob
def test_reviews(data_dir='../data/aclImdb', stop=250):
results = []
ground = []
# We make sure to test both positive and negative reviews
for sentiment in ['pos', 'neg']:
path = os.path.join(data_dir, 'test', sentiment, '*.txt')
files = glob.glob(path... | _____no_output_____ | MIT | Project/SageMaker Project.ipynb | csuquanyanfei/ML_Sagemaker_Studies_Project1 |
As an additional test, we can try sending the `test_review` that we looked at earlier. | predictor.predict(test_review) | _____no_output_____ | MIT | Project/SageMaker Project.ipynb | csuquanyanfei/ML_Sagemaker_Studies_Project1 |
Now that we know our endpoint is working as expected, we can set up the web page that will interact with it. If you don't have time to finish the project now, make sure to skip down to the end of this notebook and shut down your endpoint. You can deploy it again when you come back. Step 7 (again): Use the model for th... | predictor.endpoint | _____no_output_____ | MIT | Project/SageMaker Project.ipynb | csuquanyanfei/ML_Sagemaker_Studies_Project1 |
Once you have added the endpoint name to the Lambda function, click on **Save**. Your Lambda function is now up and running. Next we need to create a way for our web app to execute the Lambda function. Setting up API GatewayNow that our Lambda function is set up, it is time to create a new API using API Gateway that wi... | predictor.delete_endpoint() | _____no_output_____ | MIT | Project/SageMaker Project.ipynb | csuquanyanfei/ML_Sagemaker_Studies_Project1 |
Statistics Questions ```{admonition} Problem: JOIN Dataframes:class: dropdown, tipCan you tell me the ways in which 2 pandas data frames can be joined?``` ```{admonition} Solution::class: dropdownA very high level difference is that merge() is used to combine two (or more) dataframes on the basis of values of common ... | import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
def normal_sample_generator(N):
# can be done using np.random.randn or stats.norm.rvs
#x = np.random.randn(N)
x = stats.norm.rvs(size=N)
num_bins = 20
plt.hist(x, bins=num_bins, facecolor='blue', alpha=0.5)
y = np.linsp... | _____no_output_____ | MIT | _sources/contents/Python/Statistics.ipynb | mulaab/datasains |
```{admonition} Problem: [UBER] Bernoulli trial generator:class: dropdown, tipGiven a random Bernoulli trial generator, write a function to return a value sampled from a normal distribution.``` ```{admonition} Solution::class: dropdownSolution pending, [Reference material link](Given a random Bernoulli trial generator,... | # Interquartile distance is the difference between first and third quartile
# first let's generate a list of random numbers
import random
import numpy as np
li = [round(random.uniform(33.33, 66.66), 2) for i in range(50)]
print(li)
qtl_1 = np.quantile(li,.25)
qtl_3 = np.quantile(li,.75)
print("Interquartile distan... | [54.81, 65.68, 63.85, 58.29, 60.14, 53.23, 52.58, 51.62, 61.6, 57.85, 51.37, 38.7, 35.87, 33.95, 61.65, 33.59, 61.33, 44.97, 62.49, 39.67, 51.03, 45.79, 60.99, 60.49, 64.8, 46.16, 46.61, 34.06, 37.78, 56.72, 39.62, 61.38, 55.27, 40.53, 49.31, 58.95, 37.49, 34.39, 60.47, 56.12, 61.41, 34.56, 58.18, 56.35, 63.59, 50.59, ... | MIT | _sources/contents/Python/Statistics.ipynb | mulaab/datasains |
````{admonition} Problem: [GENENTECH] Imputing the mdeian:class: dropdown, tipWrite a function cheese_median to impute the median price of the selected California cheeses in place of the missing values. You may assume at least one cheese is not missing its price.Input:```pythonimport pandas as pdcheeses = {"Name": ["Bo... | import pandas as pd
cheeses = {"Name": ["Bohemian Goat", "Central Coast Bleu", "Cowgirl Mozzarella", "Cypress Grove Cheddar", "Oakdale Colby"], "Price" : [15.00, None, 30.00, None, 45.00]}
df_cheeses = pd.DataFrame(cheeses)
df_cheeses['Price'] = df_cheeses['Price'].fillna(df_cheeses['Price'].median())
df_cheeses.hea... | _____no_output_____ | MIT | _sources/contents/Python/Statistics.ipynb | mulaab/datasains |
Real Estate Price Prediction | import pandas as pd
df = pd.read_csv("data.csv")
df.head()
df['CHAS'].value_counts()
df.info()
df.describe()
%matplotlib inline
import matplotlib.pyplot as plt
df.hist(bins=50, figsize=(20,15)) | _____no_output_____ | MIT | .ipynb_checkpoints/real_estate-checkpoint.ipynb | shhubhxm/HousePricePrediction-ML_model |
train_test_split | import numpy as np
def split_train_test(data, test_ratio):
np.random.seed(42)
shuffled = np.random.permutation(len(data))
test_set_size = int(len(data) * test_ratio)
test_indices = shuffled[:test_set_size]
train_indices = shuffled[test_set_size:]
return data.iloc[train_indices], data.iloc[test_i... | _____no_output_____ | MIT | .ipynb_checkpoints/real_estate-checkpoint.ipynb | shhubhxm/HousePricePrediction-ML_model |
train_test_split from sklearn | from sklearn.model_selection import train_test_split
train_set, test_set = train_test_split(df, test_size = 0.2, random_state = 42)
print(f"The length of train dataset is: {len(train_set)}")
print(f"The length of train dataset is: {len(test_set)}")
from sklearn.model_selection import StratifiedShuffleSplit
split = Stra... | _____no_output_____ | MIT | .ipynb_checkpoints/real_estate-checkpoint.ipynb | shhubhxm/HousePricePrediction-ML_model |
Stratified learning equal splitting of zero and ones | 95/7
376/28
df = strat_train_set.copy() | _____no_output_____ | MIT | .ipynb_checkpoints/real_estate-checkpoint.ipynb | shhubhxm/HousePricePrediction-ML_model |
Corelations | from pandas.plotting import scatter_matrix
attributes = ["MEDV", "RM", "ZN" , "LSTAT"]
scatter_matrix(df[attributes], figsize = (12,8))
df.plot(kind="scatter", x="RM", y="MEDV", alpha=1) | _____no_output_____ | MIT | .ipynb_checkpoints/real_estate-checkpoint.ipynb | shhubhxm/HousePricePrediction-ML_model |
Trying out attribute combinations | df["TAXRM"] = df["TAX"]/df["RM"]
df.head()
corr_matrix = df.corr()
corr_matrix['MEDV'].sort_values(ascending=False)
# 1 means strong positive corr and -1 means strong negative corr.
# EX: if RM will increase our final result(MEDV) in prediction will also increase.
df.plot(kind="scatter", x="TAXRM", y="MEDV", alpha=1)
d... | _____no_output_____ | MIT | .ipynb_checkpoints/real_estate-checkpoint.ipynb | shhubhxm/HousePricePrediction-ML_model |
Pipeline | from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
my_pipeline = Pipeline([
('imputer', SimpleImputer(strategy="median")),
('std_scaler', StandardScaler()),
])
df_numpy = my_pipeline.fit_transform(df)
df_numpy
#Numpy array of df as m... | _____no_output_____ | MIT | .ipynb_checkpoints/real_estate-checkpoint.ipynb | shhubhxm/HousePricePrediction-ML_model |
Model Selection | from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor
# model = LinearRegression()
# model = DecisionTreeRegressor()
model = RandomForestRegressor()
model.fit(df_numpy, df_labels)
some_data = df.iloc[:5]
some_labels = df_label... | _____no_output_____ | MIT | .ipynb_checkpoints/real_estate-checkpoint.ipynb | shhubhxm/HousePricePrediction-ML_model |
Evaluating the model | from sklearn.metrics import mean_squared_error
df_predictions = model.predict(df_numpy)
mse = mean_squared_error(df_labels, df_predictions)
rmse = np.sqrt(mse)
rmse
# from sklearn.metrics import accuracy_score
# accuracy_score(some_data, some_labels, normalize=False) | _____no_output_____ | MIT | .ipynb_checkpoints/real_estate-checkpoint.ipynb | shhubhxm/HousePricePrediction-ML_model |
Cross Validation | from sklearn.model_selection import cross_val_score
scores = cross_val_score(model, df_numpy, df_labels, scoring="neg_mean_squared_error", cv=10)
rmse_scores = np.sqrt(-scores)
rmse_scores
def print_scores(scores):
print("Scores:", scores)
print("\nMean:", scores.mean())
print("\nStandard deviation:", score... | Scores: [2.79289168 2.69441597 4.40018895 2.56972379 3.33073436 2.62687167
4.77007351 3.27403209 3.38378214 3.16691711]
Mean: 3.3009631251857217
Standard deviation: 0.7076841067486248
| MIT | .ipynb_checkpoints/real_estate-checkpoint.ipynb | shhubhxm/HousePricePrediction-ML_model |
Saving Model | from joblib import dump, load
dump(model, 'final_model.joblib')
dump(model, 'final_model.sav') | _____no_output_____ | MIT | .ipynb_checkpoints/real_estate-checkpoint.ipynb | shhubhxm/HousePricePrediction-ML_model |
Testing model on test data | X_test = strat_test_set.drop("MEDV", axis=1)
Y_test = strat_test_set["MEDV"].copy()
X_test_prepared = my_pipeline.transform(X_test)
final_predictions = model.predict(X_test_prepared)
final_mse = mean_squared_error(Y_test, final_predictions)
final_rmse = np.sqrt(final_mse)
final_rmse | _____no_output_____ | MIT | .ipynb_checkpoints/real_estate-checkpoint.ipynb | shhubhxm/HousePricePrediction-ML_model |
In-Place Waveform Library UpdatesThis example notebook shows how one can update pulses data in-place without recompiling.© Raytheon BBN Technologies 2020 Set the `SAVE_WF_OFFSETS` flag in order that QGL will output a map of the waveform data within the compiled binary waveform library. | from QGL import *
import QGL
import os.path
import pickle
QGL.drivers.APS2Pattern.SAVE_WF_OFFSETS = True | _____no_output_____ | Apache-2.0 | doc/ex4_update_in_place.ipynb | gribeill/QGL |
Create the usual channel library with a couple of AWGs. | cl = ChannelLibrary(":memory:")
q1 = cl.new_qubit("q1")
aps2_1 = cl.new_APS2("BBNAPS1", address="192.168.5.101")
aps2_2 = cl.new_APS2("BBNAPS2", address="192.168.5.102")
dig_1 = cl.new_X6("X6_1", address=0)
h1 = cl.new_source("Holz1", "HolzworthHS9000", "HS9004A-009-1", power=-30)
h2 = cl.new_source("Holz2", "Holzwor... | Creating engine...
| Apache-2.0 | doc/ex4_update_in_place.ipynb | gribeill/QGL |
Compile a simple sequence. | mf = RabiAmp(cl["q1"], np.linspace(-1, 1, 11))
plot_pulse_files(mf, time=True) | Compiled 11 sequences.
<module 'QGL.drivers.APS2Pattern' from '/Users/growland/workspace/QGL/QGL/drivers/APS2Pattern.py'>
<module 'QGL.drivers.APS2Pattern' from '/Users/growland/workspace/QGL/QGL/drivers/APS2Pattern.py'>
| Apache-2.0 | doc/ex4_update_in_place.ipynb | gribeill/QGL |
Open the offsets file (in the same directory as the `.aps2` files, one per AWG slice.) | offset_f = os.path.join(os.path.dirname(mf), "Rabi-BBNAPS1.offsets")
with open(offset_f, "rb") as FID:
offsets = pickle.load(FID)
offsets | _____no_output_____ | Apache-2.0 | doc/ex4_update_in_place.ipynb | gribeill/QGL |
Let's replace every single pulse with a fixed amplitude `Utheta` | pulses = {l: Utheta(q1, amp=0.1, phase=0) for l in offsets}
wfm_f = os.path.join(os.path.dirname(mf), "Rabi-BBNAPS1.aps2")
QGL.drivers.APS2Pattern.update_wf_library(wfm_f, pulses, offsets) | _____no_output_____ | Apache-2.0 | doc/ex4_update_in_place.ipynb | gribeill/QGL |
We see that the data in the file has been updated. | plot_pulse_files(mf, time=True) | <module 'QGL.drivers.APS2Pattern' from '/Users/growland/workspace/QGL/QGL/drivers/APS2Pattern.py'>
<module 'QGL.drivers.APS2Pattern' from '/Users/growland/workspace/QGL/QGL/drivers/APS2Pattern.py'>
| Apache-2.0 | doc/ex4_update_in_place.ipynb | gribeill/QGL |
Profiling How long does this take? | %timeit mf = RabiAmp(cl["q1"], np.linspace(-1, 1, 100)) | Compiled 100 sequences.
Compiled 100 sequences.
Compiled 100 sequences.
Compiled 100 sequences.
Compiled 100 sequences.
Compiled 100 sequences.
Compiled 100 sequences.
Compiled 100 sequences.
317 ms ± 6.15 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
| Apache-2.0 | doc/ex4_update_in_place.ipynb | gribeill/QGL |
Getting the offsets is fast, and only needs to be done once | def get_offsets():
offset_f = os.path.join(os.path.dirname(mf), "Rabi-BBNAPS1.offsets")
with open(offset_f, "rb") as FID:
offsets = pickle.load(FID)
return offsets
%timeit offsets = get_offsets()
%timeit pulses = {l: Utheta(q1, amp=0.1, phase=0) for l in offsets}
wfm_f = os.path.join(os.path.dirnam... | 1.25 ms ± 19.1 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
| Apache-2.0 | doc/ex4_update_in_place.ipynb | gribeill/QGL |
Tutorial 09: InflowsThis tutorial walks you through the process of introducing inflows of vehicles into a network. Inflows allow us to simulate open networks where vehicles may enter (and potentially exit) the network. This exercise is organized as follows: in section 1 we prepare our inflows variables to support infl... | from flow.scenarios.merge import MergeScenario | _____no_output_____ | MIT | tutorials/tutorial09_inflows.ipynb | nskh/flow |
A schematic of the above network is availabe in the figure below. As we can see, the edges at the start of the main highway and the on-merge are named "inflow_highway" and "inflow_merge" respectively. These names will be important to us when we begin specifying our inflows into the network.We will also define the types... | from flow.core.vehicles import Vehicles
from flow.controllers import IDMController
# create an empty vehicles object
vehicles = Vehicles()
# add some vehicles to this object of type "human"
vehicles.add("human",
acceleration_controller=(IDMController, {}),
speed_mode="no_collide", # we use... | _____no_output_____ | MIT | tutorials/tutorial09_inflows.ipynb | nskh/flow |
Next, we are ready to import and create an empty inflows object. | from flow.core.params import InFlows
inflow = InFlows() | _____no_output_____ | MIT | tutorials/tutorial09_inflows.ipynb | nskh/flow |
The `InFlows` object is provided as an input during the scenario creation process via the `NetParams` parameter. Introducing these inflows into the network is handled by the backend scenario generation processes during instantiation of the scenario object.In order to add new inflows of vehicles of pre-defined types ont... | inflow.add(veh_type="human",
edge="inflow_highway",
vehs_per_hour=2000,
departSpeed=10,
departLane="random") | _____no_output_____ | MIT | tutorials/tutorial09_inflows.ipynb | nskh/flow |
Next, we specify a second inflow of vehicles through the on-merge lane at a rate of only 100 veh/hr. | inflow.add(veh_type="human",
edge="inflow_merge",
vehs_per_hour=100,
departSpeed=10,
departLane="random") | _____no_output_____ | MIT | tutorials/tutorial09_inflows.ipynb | nskh/flow |
2. Running Simulations with InflowsWe are now ready to test our inflows in simulation. As mentioned in section 1, the inflows are specified in the `NetParams` object, in addition to all other network-specific parameters. For the merge network, this is done as follows: | from flow.scenarios.merge import ADDITIONAL_NET_PARAMS
from flow.core.params import NetParams
additional_net_params = ADDITIONAL_NET_PARAMS.copy()
# we choose to make the main highway slightly longer
additional_net_params["pre_merge_length"] = 500
net_params = NetParams(inflows=inflow, # our inflows
... | _____no_output_____ | MIT | tutorials/tutorial09_inflows.ipynb | nskh/flow |
Finally, we execute the simulation following simulation creation techniques we learned from exercise 1 using the below code block. Running this simulation, we see an excessive number of vehicles entering from the main highway, but only a sparse number of vehicles entering from the on-merge. Nevertheless, this volume of... | from flow.core.params import SumoParams, EnvParams, InitialConfig
from flow.envs.loop.loop_accel import AccelEnv, ADDITIONAL_ENV_PARAMS
from flow.core.experiment import SumoExperiment
sumo_params = SumoParams(render=True,
sim_step=0.2)
env_params = EnvParams(additional_params=ADDITIONAL_ENV_P... | **********************************************************
**********************************************************
**********************************************************
WARNING: Inflows will cause computational performance to
significantly decrease after large number of rollouts. In
order to avoid this, set Su... | MIT | tutorials/tutorial09_inflows.ipynb | nskh/flow |
IPython magicsThis notebook is used for testing nbqa with ipython magics. | from random import randint
from IPython import get_ipython | _____no_output_____ | MIT | tests/data/notebook_with_indented_magics.ipynb | girip11/nbQA |
Cell magics | %%bash
for n in {1..10}
do
echo -n "$n "
done
%%time
import operator
def compute(operand1,operand2, bin_op):
"""Perform input binary operation over the given operands."""
return bin_op(operand1, operand2)
compute(5,1, operator.add) | CPU times: user 31 µs, sys: 4 µs, total: 35 µs
Wall time: 37.9 µs
| MIT | tests/data/notebook_with_indented_magics.ipynb | girip11/nbQA |
Help Magics | str.split??
# would this comment also be considered as magic?
str.split?
?str.splitlines | [0;31mSignature:[0m [0mstr[0m[0;34m.[0m[0msplitlines[0m[0;34m([0m[0mself[0m[0;34m,[0m [0;34m/[0m[0;34m,[0m [0mkeepends[0m[0;34m=[0m[0;32mFalse[0m[0;34m)[0m[0;34m[0m[0;34m[0m[0m
[0;31mDocstring:[0m
Return a list of the lines in the string, breaking at line boundaries.
Line breaks are no... | MIT | tests/data/notebook_with_indented_magics.ipynb | girip11/nbQA |
Shell magics | !grep -r '%%HTML' . | wc -l
flake8_version = !pip list 2>&1 | grep flake8
if flake8_version:
print(flake8_version) | ['flake8 3.8.4']
| MIT | tests/data/notebook_with_indented_magics.ipynb | girip11/nbQA |
Line magics | %time randint(5,10)
if __debug__:
%time compute(5,1, operator.mul)
%time get_ipython().run_line_magic("lsmagic", "")
import pprint
import sys
%time pretty_print_object = pprint.PrettyPrinter(\
indent=4, width=80, stream=sys.stdout, compact=True, depth=5\
) | CPU times: user 29 µs, sys: 0 ns, total: 29 µs
Wall time: 33.4 µs
| MIT | tests/data/notebook_with_indented_magics.ipynb | girip11/nbQA |
$BA_i \sim Beta(81,219)$$y_i \sim Bin(AB_i,BA_i)$$i=1,2,...,8$ | #https://mc-stan.org/users/documentation/case-studies/rstan_workflow.html
#https://people.duke.edu/~ccc14/sta-663/PyStan.html
#http://varianceexplained.org/statistics/beta_distribution_and_baseball/
model_code = '''
data {
int<lower=0> N;
int<lower=0> at_bats[N];
int<lower=0> hits[N];
real<lower=0> A;
real<l... | _____no_output_____ | MIT | beta_binomial_baseball.ipynb | thomasmartins/pystan_misc |
Introduction to Pandas | import pandas
pandas.__version__
import pandas as pd | _____no_output_____ | MIT | code_listings/03.00-Introduction-to-Pandas.ipynb | cesar-rocha/PythonDataScienceHandbook |
[](https://colab.research.google.com/github/ourownstory/neural_prophet/blob/master/example_notebooks/sub_daily_data_yosemite_temps.ipynb) Sub-daily dataNeuralProphet can make forecasts for time series with sub-daily observations by passing in a ... | if 'google.colab' in str(get_ipython()):
!pip install git+https://github.com/ourownstory/neural_prophet.git # may take a while
#!pip install neuralprophet # much faster, but may not have the latest upgrades/bugfixes
data_location = "https://raw.githubusercontent.com/ourownstory/neural_prophet/master/"
else:... | _____no_output_____ | MIT | example_notebooks/sub_daily_data_yosemite_temps.ipynb | aws-kh/neural_prophet |
Now we will attempt to forecast the next 7 days. The `5min` data resulution means that we have `60/5*24=288` daily values. Thus, we want to forecast `7*288` periods ahead.Using some common sense, we set:* First, we disable weekly seasonality, as nature does not follow the human week's calendar.* Second, we disable chan... | m = NeuralProphet(
n_changepoints=0,
weekly_seasonality=False,
)
metrics = m.fit(df, freq='5min')
future = m.make_future_dataframe(df, periods=7*288, n_historic_predictions=len(df))
forecast = m.predict(future)
fig = m.plot(forecast)
# fig_comp = m.plot_components(forecast)
fig_param = m.plot_parameters() | INFO - (NP.forecaster._handle_missing_data) - 12 NaN values in column y were auto-imputed.
INFO - (NP.utils.set_auto_seasonalities) - Disabling yearly seasonality. Run NeuralProphet with yearly_seasonality=True to override this.
INFO - (NP.config.set_auto_batch_epoch) - Auto-set batch_size to 128
INFO - (NP.config.set_... | MIT | example_notebooks/sub_daily_data_yosemite_temps.ipynb | aws-kh/neural_prophet |
The daily seasonality seems to make sense, when we account for the time being recorded in GMT, while Yosemite local time is GMT-8. Improving trend and seasonality As we have `288` daily values recorded, we can increase the flexibility of `daily_seasonality`, without danger of overfitting. Further, we may want to re-vis... | m = NeuralProphet(
changepoints_range=0.95,
n_changepoints=50,
trend_reg=1.5,
weekly_seasonality=False,
daily_seasonality=10,
)
metrics = m.fit(df, freq='5min')
future = m.make_future_dataframe(df, periods=60//5*24*7, n_historic_predictions=len(df))
forecast = m.predict(future)
fig = m.plot(forecast... | INFO - (NP.config.__post_init__) - Note: Trend changepoint regularization is experimental.
INFO - (NP.forecaster._handle_missing_data) - 12 NaN values in column y were auto-imputed.
INFO - (NP.utils.set_auto_seasonalities) - Disabling yearly seasonality. Run NeuralProphet with yearly_seasonality=True to override this.
... | MIT | example_notebooks/sub_daily_data_yosemite_temps.ipynb | aws-kh/neural_prophet |
Deep Deterministic Policy Gradients (DDPG)---In this notebook, we train DDPG with OpenAI Gym's Pendulum-v0 environment. 1. Import the Necessary Packages | import gym
import random
import torch
import numpy as np
from collections import deque
import matplotlib.pyplot as plt
%matplotlib inline
from ddpg_agent import Agent | _____no_output_____ | MIT | ddpg-pendulum/DDPG.ipynb | elexira/deep-reinforcement-learning |
2. Instantiate the Environment and Agent | env = gym.make('Pendulum-v0')
env.seed(2)
agent = Agent(state_size=3, action_size=1, random_seed=2) | _____no_output_____ | MIT | ddpg-pendulum/DDPG.ipynb | elexira/deep-reinforcement-learning |
ObservationType: Box(3)|Num |Observation |Min| Max|| --- | --- | --- |--- |0 | cos(theta) | -1.0 |1.0|1 | sin(theta) |-1.0 |1.0|2 | theta dot |-8.0 |8.0|ActionsType: Box(1)| Num | Action | Min | Max| | --- | --- | --- |--- || 0 | Joint effort | -2.0 | 2.0| 3. Train the Agent with DDPG | def ddpg(n_episodes=100, max_t=300, print_every=100):
scores_deque = deque(maxlen=print_every)
scores = []
for i_episode in range(1, n_episodes+1):
state = env.reset()
agent.reset()
score = 0
for t in range(max_t):
action = agent.act(state)
next_state,... | Episode 100 Average Score: -595.74
| MIT | ddpg-pendulum/DDPG.ipynb | elexira/deep-reinforcement-learning |
4. Watch a Smart Agent! | agent.actor_local.load_state_dict(torch.load('checkpoint_actor.pth'))
agent.critic_local.load_state_dict(torch.load('checkpoint_critic.pth'))
state = env.reset()
for t in range(500):
action = agent.act(state, add_noise=False)
env.render()
state, reward, done, _ = env.step(action)
if done:
break... | _____no_output_____ | MIT | ddpg-pendulum/DDPG.ipynb | elexira/deep-reinforcement-learning |
6. ExploreIn this exercise, we have provided a sample DDPG agent and demonstrated how to use it to solve an OpenAI Gym environment. To continue your learning, you are encouraged to complete any (or all!) of the following tasks:- Amend the various hyperparameters and network architecture to see if you can get your age... | int(1e5) | _____no_output_____ | MIT | ddpg-pendulum/DDPG.ipynb | elexira/deep-reinforcement-learning |
Classes and Objects in PythonEstimated time needed: **40** minutes ObjectivesAfter completing this lab you will be able to:- Work with classes and objects- Identify and define attributes and methods Table of Contents Introduction to Classes and Objects Creating... | # Import the library
import matplotlib.pyplot as plt
%matplotlib inline | _____no_output_____ | FSFAP | 4_Python for Data Science, AI & Development/PY0101EN-3-5-Classes.ipynb | lebinh97/IBM-DataScience-Capstone |
The first step in creating your own class is to use the class keyword, then the name of the class as shown in Figure 4. In this course the class parent will always be object: Figure 4: Creating a class Circle. The next step is a special method called a constructor __init__, which is used to initialize the object. Th... | # Create a class Circle
class Circle(object):
# Constructor
def __init__(self, radius=3, color='blue'):
self.radius = radius
self.color = color
# Method
def add_radius(self, r):
self.radius = self.radius + r
return(self.radius)
# Method
def drawCi... | _____no_output_____ | FSFAP | 4_Python for Data Science, AI & Development/PY0101EN-3-5-Classes.ipynb | lebinh97/IBM-DataScience-Capstone |
Creating an instance of a class Circle Let’s create the object RedCircle of type Circle to do the following: | # Create an object RedCircle
RedCircle = Circle(10, 'red') | _____no_output_____ | FSFAP | 4_Python for Data Science, AI & Development/PY0101EN-3-5-Classes.ipynb | lebinh97/IBM-DataScience-Capstone |
We can use the dir command to get a list of the object's methods. Many of them are default Python methods. | # Find out the methods can be used on the object RedCircle
dir(RedCircle) | _____no_output_____ | FSFAP | 4_Python for Data Science, AI & Development/PY0101EN-3-5-Classes.ipynb | lebinh97/IBM-DataScience-Capstone |
We can look at the data attributes of the object: | # Print the object attribute radius
RedCircle.radius
# Print the object attribute color
RedCircle.color | _____no_output_____ | FSFAP | 4_Python for Data Science, AI & Development/PY0101EN-3-5-Classes.ipynb | lebinh97/IBM-DataScience-Capstone |
We can change the object's data attributes: | # Set the object attribute radius
RedCircle.radius = 1
RedCircle.radius | _____no_output_____ | FSFAP | 4_Python for Data Science, AI & Development/PY0101EN-3-5-Classes.ipynb | lebinh97/IBM-DataScience-Capstone |
We can draw the object by using the method drawCircle(): | # Call the method drawCircle
RedCircle.drawCircle() | _____no_output_____ | FSFAP | 4_Python for Data Science, AI & Development/PY0101EN-3-5-Classes.ipynb | lebinh97/IBM-DataScience-Capstone |
We can increase the radius of the circle by applying the method add_radius(). Let increases the radius by 2 and then by 5: | # Use method to change the object attribute radius
print('Radius of object:',RedCircle.radius)
RedCircle.add_radius(2)
print('Radius of object of after applying the method add_radius(2):',RedCircle.radius)
RedCircle.add_radius(5)
print('Radius of object of after applying the method add_radius(5):',RedCircle.radius)
Re... | Radius of object: 1
Radius of object of after applying the method add_radius(2): 3
Radius of object of after applying the method add_radius(5): 8
Radius of object of after applying the method add radius(6): 14
| FSFAP | 4_Python for Data Science, AI & Development/PY0101EN-3-5-Classes.ipynb | lebinh97/IBM-DataScience-Capstone |
Let’s create a blue circle. As the default colour is blue, all we have to do is specify what the radius is: | # Create a blue circle with a given radius
BlueCircle = Circle(radius=100) | _____no_output_____ | FSFAP | 4_Python for Data Science, AI & Development/PY0101EN-3-5-Classes.ipynb | lebinh97/IBM-DataScience-Capstone |
As before we can access the attributes of the instance of the class by using the dot notation: | # Print the object attribute radius
BlueCircle.radius
# Print the object attribute color
BlueCircle.color | _____no_output_____ | FSFAP | 4_Python for Data Science, AI & Development/PY0101EN-3-5-Classes.ipynb | lebinh97/IBM-DataScience-Capstone |
We can draw the object by using the method drawCircle(): | # Call the method drawCircle
BlueCircle.drawCircle() | _____no_output_____ | FSFAP | 4_Python for Data Science, AI & Development/PY0101EN-3-5-Classes.ipynb | lebinh97/IBM-DataScience-Capstone |
Compare the x and y axis of the figure to the figure for RedCircle; they are different. The Rectangle Class Let's create a class rectangle with the attributes of height, width and color. We will only add the method to draw the rectangle object: | # Create a new Rectangle class for creating a rectangle object
class Rectangle(object):
# Constructor
def __init__(self, width=2, height=3, color='r'):
self.height = height
self.width = width
self.color = color
# Method
def drawRectangle(self):
plt.gca().add_p... | _____no_output_____ | FSFAP | 4_Python for Data Science, AI & Development/PY0101EN-3-5-Classes.ipynb | lebinh97/IBM-DataScience-Capstone |
Let’s create the object SkinnyBlueRectangle of type Rectangle. Its width will be 2 and height will be 3, and the color will be blue: | # Create a new object rectangle
SkinnyBlueRectangle = Rectangle(2, 10, 'blue') | _____no_output_____ | FSFAP | 4_Python for Data Science, AI & Development/PY0101EN-3-5-Classes.ipynb | lebinh97/IBM-DataScience-Capstone |
As before we can access the attributes of the instance of the class by using the dot notation: | # Print the object attribute height
SkinnyBlueRectangle.height
# Print the object attribute width
SkinnyBlueRectangle.width
# Print the object attribute color
SkinnyBlueRectangle.color | _____no_output_____ | FSFAP | 4_Python for Data Science, AI & Development/PY0101EN-3-5-Classes.ipynb | lebinh97/IBM-DataScience-Capstone |
We can draw the object: | # Use the drawRectangle method to draw the shape
SkinnyBlueRectangle.drawRectangle() | _____no_output_____ | FSFAP | 4_Python for Data Science, AI & Development/PY0101EN-3-5-Classes.ipynb | lebinh97/IBM-DataScience-Capstone |
Let’s create the object FatYellowRectangle of type Rectangle : | # Create a new object rectangle
FatYellowRectangle = Rectangle(20, 5, 'yellow') | _____no_output_____ | FSFAP | 4_Python for Data Science, AI & Development/PY0101EN-3-5-Classes.ipynb | lebinh97/IBM-DataScience-Capstone |
We can access the attributes of the instance of the class by using the dot notation: | # Print the object attribute height
FatYellowRectangle.height
# Print the object attribute width
FatYellowRectangle.width
# Print the object attribute color
FatYellowRectangle.color | _____no_output_____ | FSFAP | 4_Python for Data Science, AI & Development/PY0101EN-3-5-Classes.ipynb | lebinh97/IBM-DataScience-Capstone |
We can draw the object: | # Use the drawRectangle method to draw the shape
FatYellowRectangle.drawRectangle() | _____no_output_____ | FSFAP | 4_Python for Data Science, AI & Development/PY0101EN-3-5-Classes.ipynb | lebinh97/IBM-DataScience-Capstone |
Exercises Text Analysis You have been recruited by your friend, a linguistics enthusiast, to create a utility tool that can perform analysis on a given piece of text. Complete the class'analysedText' with the following methods - Constructor - Takes argument 'text',makes it lower case and removes all punctuation.... | class analysedText(object):
def __init__ (self, text):
reArrText = text.lower()
reArrText = reArrText.replace('.','').replace('!','').replace(',','').replace('?','')
self.fmtText = reArrText
def freqAll(self):
... | _____no_output_____ | FSFAP | 4_Python for Data Science, AI & Development/PY0101EN-3-5-Classes.ipynb | lebinh97/IBM-DataScience-Capstone |
Execute the block below to check your progress. | import sys
sampleMap = {'eirmod': 1,'sed': 1, 'amet': 2, 'diam': 5, 'consetetur': 1, 'labore': 1, 'tempor': 1, 'dolor': 1, 'magna': 2, 'et': 3, 'nonumy': 1, 'ipsum': 1, 'lorem': 2}
def testMsg(passed):
if passed:
return 'Test Passed'
else :
return 'Test Failed'
print("Constructor: ")
try:
s... | Constructor:
Test Passed
freqAll:
Test Passed
freqOf:
Test Passed
| FSFAP | 4_Python for Data Science, AI & Development/PY0101EN-3-5-Classes.ipynb | lebinh97/IBM-DataScience-Capstone |
Analyzing the Effects of Non-Academic Features on Student Performance | # For reading data sets
import pandas
# For lots of awesome things
import numpy as np
# Need this for LabelEncoder
from sklearn import preprocessing
# For building our net
import keras
# For plotting
import matplotlib.pyplot as plt
%matplotlib inline | _____no_output_____ | MIT | LSTM1.ipynb | CSCI4850/S19-team3-project |
Read in data Data is seperated by a semicolon (delimiter=";") containing column names as the first row of the file (header = 0). | # Read in student data
student_data = np.array(pandas.read_table("./student-por.csv",
delimiter=";", header=0))
# Display student data
student_data | _____no_output_____ | MIT | LSTM1.ipynb | CSCI4850/S19-team3-project |
Determine what the column labels are... | # Descriptions for each feature (found in the header)
feature_descrips = np.array(pandas.read_csv("./student-por.csv",
delimiter=";", header=None, nrows=1))
# Display descriptions
print(feature_descrips) | [['school' 'sex' 'age' 'address' 'famsize' 'Pstatus' 'Medu' 'Fedu' 'Mjob'
'Fjob' 'reason' 'guardian' 'traveltime' 'studytime' 'failures'
'schoolsup' 'famsup' 'paid' 'activities' 'nursery' 'higher' 'internet'
'romantic' 'famrel' 'freetime' 'goout' 'Dalc' 'Walc' 'health'
'absences' 'G1' 'G2' 'G3']]
| MIT | LSTM1.ipynb | CSCI4850/S19-team3-project |
...and give them clearer descriptions. | # More detailed descriptions
feature_descrips = np.array(["School", "Sex", "Age", "Urban or Rural Address", "Family Size",
"Parent's Cohabitation status", "Mother's Education", "Father's Education",
"Mother's Job", "Father's Job", "Reason for Choosing School", ... | _____no_output_____ | MIT | LSTM1.ipynb | CSCI4850/S19-team3-project |
Data Cleanup Shuffle data We sampled 2 schools, and right now our data has each school grouped together. We need to get rid of this grouping for training later down the road. | # Shuffle the data!
np.random.shuffle(student_data)
student_data | _____no_output_____ | MIT | LSTM1.ipynb | CSCI4850/S19-team3-project |
Alphabetically classify scores Because our data is sampled from Portugal, we have to modify their scoring system a bit to represent something more like ours. 0 = F 1 = D 2 = C 3 = B 4 = A | # Array holding final scores for every student
scores = student_data[:,32]
# Iterate through list of scores, changing them from a 0-19 value
## to a 0-4 value (representing F-A)
for i in range(len(scores)):
if(scores[i] > 18):
scores[i] = 4
elif(scores[i] > 16):
scores[i] = 3
elif(scores[i]... | _____no_output_____ | MIT | LSTM1.ipynb | CSCI4850/S19-team3-project |
Encoding non-numeric data to integers | # One student sample
student_data[0,:] | _____no_output_____ | MIT | LSTM1.ipynb | CSCI4850/S19-team3-project |
We have some qualitative data from the questionaire that needs to be converted to represent numbers. | # Label Encoder
le = preprocessing.LabelEncoder()
# Columns that hold non-numeric data
indices = np.array([0,1,3,4,5,8,9,10,11,15,16,17,18,19,20,21,22])
# Transform the non-numeric data in these columns to integers
for i in range(len(indices)):
column = indices[i]
le.fit(student_data[:,column])
student_da... | _____no_output_____ | MIT | LSTM1.ipynb | CSCI4850/S19-team3-project |
Encoding 0's to -1 for binomial data. We want our weights to change because 0 represents something! Therefore, we need to encode 0's to -1's so the weights will change with that input. | # Columns that hold binomial data
indices = np.array([0,1,3,4,5,15,16,17,18,19,20,21,22])
# Change 0's to -1's
for i in range(len(indices)):
column = indices[i]
# values of current feature
feature = student_data[:,column]
# change values to -1 if equal to 0
feature = np.where(feature==0, ... | _____no_output_____ | MIT | LSTM1.ipynb | CSCI4850/S19-team3-project |
Standardizing the nominal and numerical data. We need our input to matter equally (Everyone is important!). We do this by standardizing our data (get a mean of 0 and a stardard deviation of 1). | scaler = preprocessing.StandardScaler()
temp = student_data[:,[2,6,7,8,9,10,11,12,13,14,23,24,25,26,27,28,29,30,31]]
print(student_data[0,:])
Standardized = scaler.fit_transform(temp)
print('Mean:', round(Standardized.mean()))
print('Standard deviation:', Standardized.std())
student_data[:,[2,6,7,8,9,10,11,12,13,14,23,... | _____no_output_____ | MIT | LSTM1.ipynb | CSCI4850/S19-team3-project |
Convert results to one-hot encoding | # Final grades
results = student_data[:,32]
# Take a look at first 5 final grades
print("First 5 final grades:", results[0:5])
# All unique values for final grades (0-4 representing F-A)
possible_results = np.unique(student_data[:,32]).T
print("All possible results:", possible_results)
# One-hot encode final grades (... | _____no_output_____ | MIT | LSTM1.ipynb | CSCI4850/S19-team3-project |
Model Building Now let's create a function that will build a model for us. This will come in handy later on. Our model will have two hidden layers. The first hidden layer will have an input size of 800, and the second will have an input size of 400. The optimizer that we are using is adamax which is good at ignoring ... | # Function to create network given model
def create_network(model):
# Specify input/output size
input_size = x.shape[1]
output_size = y.shape[1]
# Create the hidden layer
model.add(keras.layers.Dense(800, input_dim = input_size, activation = 'relu'))
# Additional hidden layer
model.add(ker... | _____no_output_____ | MIT | LSTM1.ipynb | CSCI4850/S19-team3-project |
Initial Test of the Network | # Split data into training and testing data
x_train = x[0:518,:]
x_test = x[519:649,:]
y_train = y[0:518,:]
y_test = y[519:649,:]
# Train on training data!
# We're saving this information in the variable -history- so we can take a look at it later
history = model.fit(x_train, y_train,
batch_size =... | _____no_output_____ | MIT | LSTM1.ipynb | CSCI4850/S19-team3-project |
Training and Testing Without Individual Features | # Analyze the effects of removing one feature on training
def remove_and_analyze(feature):
# Told you those feature descriptions would be useful
print("Without feature", feature, ":", feature_descrips[feature])
# Create feed-forward network
model = keras.Sequential()
create_network(model)
... | Without feature 0 : School
Test loss: 0.1621086014179477
Test accuracy: 0.9461538461538461
| MIT | LSTM1.ipynb | CSCI4850/S19-team3-project |
Training and Testing Without Five Features | # Delete the five features that most negatively impact accuracy
x = np.delete(student_data, 21, axis = 1)
x = np.delete(x, 20, axis = 1)
x = np.delete(x, 9, axis = 1)
x = np.delete(x, 8, axis = 1)
x = np.delete(x, 7, axis = 1)
# Create feed-forward network
model = keras.Sequential()
create_network(model)
# Split data... | Test loss: 0.1731401116976765
Test accuracy: 0.9307692307692308
| MIT | LSTM1.ipynb | CSCI4850/S19-team3-project |
Grade Distribution Analysis | # Function for analyzing the percent of students with each grade [F,D,C,B,A]
def analyze(array):
# To hold the total number of students with a certain final grade
# Index 0 - F. Index 4 - A
sums = np.array([0,0,0,0,0])
# Iterate through array. Update sums according to whether a student got a f... | _____no_output_____ | MIT | LSTM1.ipynb | CSCI4850/S19-team3-project |
Family Educational Support | # Array holding final grades of all students who have family educational support
fam_sup = []
# Array holding final grades of all students who have family educational support
no_fam_sup = []
# Iterate through all student samples
for i in range(student_data.shape[0]):
# Does the student have family educational... | _____no_output_____ | MIT | LSTM1.ipynb | CSCI4850/S19-team3-project |
Family Educational Support | analyze(fam_sup) | _____no_output_____ | MIT | LSTM1.ipynb | CSCI4850/S19-team3-project |
No Family Educational Support | analyze(no_fam_sup) | _____no_output_____ | MIT | LSTM1.ipynb | CSCI4850/S19-team3-project |
Reason for choosing school | # Each array holds the grades of students who chose to go to their school for that reason
# Close to home
reason1 = []
# School reputation
reason2 = []
# Course prefrence
reason3 = []
# Other
reason4 = []
# Values that represent these unique reasons. They are not integer numbers like in the previous
## example. They'r... | _____no_output_____ | MIT | LSTM1.ipynb | CSCI4850/S19-team3-project |
Reason 1: Close to Home | analyze(reason1) | _____no_output_____ | MIT | LSTM1.ipynb | CSCI4850/S19-team3-project |
Reason 2: School Reputation | analyze(reason2) | _____no_output_____ | MIT | LSTM1.ipynb | CSCI4850/S19-team3-project |
Reason 3: Course Prefrence | analyze(reason3) | _____no_output_____ | MIT | LSTM1.ipynb | CSCI4850/S19-team3-project |
Reason 4: Other | analyze(reason4) | _____no_output_____ | MIT | LSTM1.ipynb | CSCI4850/S19-team3-project |
Frequency of Going Out With Friends | # Each array holds the grades of students who go out with friends for that specified amount of time
# (1 - very low, 5 - very high)
go_out1 = []
go_out2 = []
go_out3 = []
go_out4 = []
go_out5 = []
# Floating point values representing frequency
unique = np.unique(student_data[:,25])
# Iterate through all student samp... | _____no_output_____ | MIT | LSTM1.ipynb | CSCI4850/S19-team3-project |
Free Time after School | # Each array holds the grades of students who have the specified amount of free time after school
# (1 - very low, 5 - very high)
free1 = []
free2 = []
free3 = []
free4 = []
free5 = []
# Floating point values representing frequency
unique = np.unique(student_data[:,24])
# Iterate through all student samples and appe... | _____no_output_____ | MIT | LSTM1.ipynb | CSCI4850/S19-team3-project |
Paid Classes | # Array holding final grades of all students who have extra paid classes
paid_class = []
# Array holding final grades of all students who do not have extra paid classes
no_paid_class = []
# Iterate through all student samples and append final grades to corresponding arrays
for i in range(student_data.shape[0]):
... | _____no_output_____ | MIT | LSTM1.ipynb | CSCI4850/S19-team3-project |
Extra Paid Classes | analyze(paid_class) | _____no_output_____ | MIT | LSTM1.ipynb | CSCI4850/S19-team3-project |
No Extra Paid Classes | analyze(no_paid_class) | _____no_output_____ | MIT | LSTM1.ipynb | CSCI4850/S19-team3-project |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.