diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md index e387154b..ccf5579f 100644 --- a/.github/pull_request_template.md +++ b/.github/pull_request_template.md @@ -1,12 +1,12 @@ --- name: b) New feature -about: Select this if you're adding a new feature to verve. +about: Select this if you're adding a new feature to libra. labels: feature --- This pull request closes #issue_number_here . @@ -24,4 +24,4 @@ including tests for the new feature you added. - [ ] I updated the docs. -This pull request adds a new feature to verve. @Palashio, could you please take a look at it? +This pull request adds a new feature to libra. @Palashio, could you please take a look at it? diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index c5b225da..714ea214 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1 +1 @@ -Guidelines for contributing to verve will come soon. +Guidelines for contributing to libra will come soon. diff --git a/README.md b/README.md index 61a30ed0..446d93b2 100644 --- a/README.md +++ b/README.md @@ -1,15 +1,15 @@ ## INITIAL BUILD RELEASE DATE: JULY 16TH. in progress....this README.md is out of date. Documentation site coming soon. -# Verve: Deep Learning fluent in one-liners +# libra: Deep Learning fluent in one-liners -Verve is a deep learning API that allows users to use machine learning in their workflows in fluent one-liners. It is written in Python and TensorFlow and makes training neural networks as simple as a one line function call. It was written to make deep learning as simple as possible to every user. +libra is a deep learning API that allows users to use machine learning in their workflows in fluent one-liners. It is written in Python and TensorFlow and makes training neural networks as simple as a one line function call. It was written to make deep learning as simple as possible to every user. *** ## Guiding Principles ## - * **Beginner Friendly.** verve is an API designed to be used by developers with no deep learning experience whatsoever. It is built so that users with no knowledge in preprocessing, modeling, or tuning can build high-performance models with ease without worrying about the details of implementation. + * **Beginner Friendly.** libra is an API designed to be used by developers with no deep learning experience whatsoever. It is built so that users with no knowledge in preprocessing, modeling, or tuning can build high-performance models with ease without worrying about the details of implementation. - * **Quick Integration.** With the recent rise of machine learning on the cloud, the developer community has failed to make easy-to-use platforms that exist locally and integrate directly into workflows. verve allows users to develop models directly in programs with hundreds of API endpoints without having to worry about the transition to the cloud. + * **Quick Integration.** With the recent rise of machine learning on the cloud, the developer community has failed to make easy-to-use platforms that exist locally and integrate directly into workflows. libra allows users to develop models directly in programs with hundreds of API endpoints without having to worry about the transition to the cloud. * **Automation.** End-to-end pipelines containing hundreds of processes are automatically run for the user. The developer only has to consider what they want to accomplish from the task and the location of their initial dataset. @@ -54,7 +54,7 @@ Generally, all queries have the same structure. You should always be passing an Let's start with the most basic query. This will build a feed-forward network for a continuous label that you specify. ```python -import verve +import libra newClient = client('dataset') newClient.regression_query_ann('Model the median house value') @@ -235,9 +235,9 @@ If you'd like to get the best pipeline for dimensionality reduction you can call dimensionality_reduc("I want to estimate number of crime", path_to_dataset) ``` -Instructions like "I want to model x" are provided in the dimensionality reduction pipeline because it identifies which prediction objective you would like to maximize the accuracy for. Providing this instruction helps verve provide users with the best modification pipeline. +Instructions like "I want to model x" are provided in the dimensionality reduction pipeline because it identifies which prediction objective you would like to maximize the accuracy for. Providing this instruction helps libra provide users with the best modification pipeline. -verve current supports feature importance identification using random forest regressor, indepedent component analysis, and principle component analysis. The output of the dimensionalityReduc() function should look something like this: +libra current supports feature importance identification using random forest regressor, indepedent component analysis, and principle component analysis. The output of the dimensionalityReduc() function should look something like this: ``` Baseline Accuracy: 0.9752906976744186 @@ -299,7 +299,7 @@ If this does not converge a message will be displayed for users to warn them by ## Process Logger ## -verve will automatically output the current process running in a hierarchial format like this: +libra will automatically output the current process running in a hierarchial format like this: ``` loading dataset... @@ -321,9 +321,9 @@ A quiet mode feature is currently being implemented. ## Pipelining for Contributors ## -In order to help make verve extensible, a process pipeliner has been implemented to help contributors easily test their newly-developed modules. +In order to help make libra extensible, a process pipeliner has been implemented to help contributors easily test their newly-developed modules. -Let's say you've developed a different preprocesser for data that you want to test before integrating it into verve's primary workflow. This is the process to test it out: +Let's say you've developed a different preprocesser for data that you want to test before integrating it into libra's primary workflow. This is the process to test it out: First, you want to initialize your base parameters, which are your instructions, the path to your dataset, and any other information your new function might require. @@ -359,7 +359,7 @@ All model information should be stored in ```init_params```. If you'd like to mo *** ## Instructions ## -verve uses intelligent natural language processing to analyze user instructions and match it with a column in user datasets. +libra uses intelligent natural language processing to analyze user instructions and match it with a column in user datasets. 1. [Textblob](https://textblob.readthedocs.io/en/dev/), a part of speech recognition algorithm, is used to identify parts of speech. 2. A self-developed part-of-speech deciphering algorithm is used to extract relevant parts of a sentence. 3. Masks are generated to represent all words as tensors in order for easy comparison @@ -368,4 +368,4 @@ verve uses intelligent natural language processing to analyze user instructions ## Example Projects ## -To get started, take a look at some of these examples of data science projects analyzing datasets using verve. +To get started, take a look at some of these examples of data science projects analyzing datasets using libra. diff --git a/docker/Dockerfile b/docker/Dockerfile index 1c3576f6..bea8c308 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -27,7 +27,7 @@ RUN wget --quiet --no-check-certificate https://repo.continuum.io/miniconda/Mini echo export PATH=$CONDA_DIR/bin:'$PATH' > /etc/profile.d/conda.sh # Install Python packages and keras -ENV NB_USER verve +ENV NB_USER libra ENV NB_UID 1000 RUN useradd -m -s /bin/bash -N -u $NB_UID $NB_USER && \ @@ -75,7 +75,7 @@ RUN conda install -y python=${python_version} && \ theano \ mkdocs \ && \ - git clone git://github.com/Palashio/verve.git /src && pip install -e /src[tests] && pip install git+git://github.com/Palashio/verve.git && \ + git clone git://github.com/Palashio/libra.git /src && pip install -e /src[tests] && pip install git+git://github.com/Palashio/libra.git && \ conda clean -yt; exit 0 ENV LC_ALL=C.UTF-8 diff --git a/docker/Makefile b/docker/Makefile index 97b33b23..13d018b0 100644 --- a/docker/Makefile +++ b/docker/Makefile @@ -13,16 +13,16 @@ TEST=tests/ SRC?=$(shell dirname `pwd`) build: - docker build -t verve --build-arg python_version=$(PYTHON_VERSION) --build-arg cuda_version=$(CUDA_VERSION) --build-arg cudnn_version=$(CUDNN_VERSION) -f $(DOCKER_FILE) . + docker build -t libra --build-arg python_version=$(PYTHON_VERSION) --build-arg cuda_version=$(CUDA_VERSION) --build-arg cudnn_version=$(CUDNN_VERSION) -f $(DOCKER_FILE) . bash: build - $(DOCKER) run -it -v $(SRC):/src/workspace -v $(DATA):/data --env verve_BACKEND=$(BACKEND) verve bash + $(DOCKER) run -it -v $(SRC):/src/workspace -v $(DATA):/data --env libra_BACKEND=$(BACKEND) libra bash ipython: build - $(DOCKER) run -it -v $(SRC):/src/workspace -v $(DATA):/data --env verve_BACKEND=$(BACKEND) verve ipython + $(DOCKER) run -it -v $(SRC):/src/workspace -v $(DATA):/data --env libra_BACKEND=$(BACKEND) libra ipython notebook: build - $(DOCKER) run -it -v $(SRC):/src/workspace -v $(DATA):/data --net=host --env verve_BACKEND=$(BACKEND) verve + $(DOCKER) run -it -v $(SRC):/src/workspace -v $(DATA):/data --net=host --env libra_BACKEND=$(BACKEND) libra test: build - $(DOCKER) run -it -v $(SRC):/src/workspace -v $(DATA):/data --env verve_BACKEND=$(BACKEND) verve py.test $(TEST) + $(DOCKER) run -it -v $(SRC):/src/workspace -v $(DATA):/data --env libra_BACKEND=$(BACKEND) libra py.test $(TEST) diff --git a/docker/README.md b/docker/README.md index 318f6205..097c132a 100644 --- a/docker/README.md +++ b/docker/README.md @@ -1,7 +1,7 @@ -# Using verve via Docker +# Using libra via Docker This directory contains `Dockerfile` to make it easy to get up and running with -verve via [Docker](http://www.docker.com/). +libra via [Docker](http://www.docker.com/). ## Installing Docker diff --git a/verve/__init__.py b/libra/__init__.py similarity index 100% rename from verve/__init__.py rename to libra/__init__.py diff --git a/verve/data_generation/__init__.py b/libra/data_generation/__init__.py similarity index 100% rename from verve/data_generation/__init__.py rename to libra/data_generation/__init__.py diff --git a/verve/data_generation/dataGen.py b/libra/data_generation/dataGen.py similarity index 100% rename from verve/data_generation/dataGen.py rename to libra/data_generation/dataGen.py diff --git a/verve/data_generation/dataset_labelmatcher.py b/libra/data_generation/dataset_labelmatcher.py similarity index 100% rename from verve/data_generation/dataset_labelmatcher.py rename to libra/data_generation/dataset_labelmatcher.py diff --git a/verve/data_generation/grammartree.py b/libra/data_generation/grammartree.py similarity index 100% rename from verve/data_generation/grammartree.py rename to libra/data_generation/grammartree.py diff --git a/verve/dev_pipeliner/__init__.py b/libra/dev_pipeliner/__init__.py similarity index 100% rename from verve/dev_pipeliner/__init__.py rename to libra/dev_pipeliner/__init__.py diff --git a/verve/dev_pipeliner/pipeline_sample.py b/libra/dev_pipeliner/pipeline_sample.py similarity index 100% rename from verve/dev_pipeliner/pipeline_sample.py rename to libra/dev_pipeliner/pipeline_sample.py diff --git a/verve/dev_pipeliner/regression_split_functions.py b/libra/dev_pipeliner/regression_split_functions.py similarity index 89% rename from verve/dev_pipeliner/regression_split_functions.py rename to libra/dev_pipeliner/regression_split_functions.py index e12fbf9c..6c965e3d 100644 --- a/verve/dev_pipeliner/regression_split_functions.py +++ b/libra/dev_pipeliner/regression_split_functions.py @@ -18,23 +18,23 @@ from sklearn.preprocessing import OneHotEncoder, StandardScaler from tensorflow import keras from tensorflow.python.keras.layers import Dense, Input -from verve.data_generation.dataset_labelmatcher import get_similar_column, get_similar_model +from libra.data_generation.dataset_labelmatcher import get_similar_column, get_similar_model from keras.callbacks import EarlyStopping from matplotlib import pyplot -from verve.data_generation.grammartree import get_value_instruction -from verve.preprocessing.data_preprocesser import structured_preprocesser -from verve.modeling.prediction_model_creation import get_keras_model_reg -from verve.modeling.prediction_model_creation import get_keras_model_class +from libra.data_generation.grammartree import get_value_instruction +from libra.preprocessing.data_preprocesser import structured_preprocesser +from libra.modeling.prediction_model_creation import get_keras_model_reg +from libra.modeling.prediction_model_creation import get_keras_model_class from keras.utils import to_categorical from keras.utils import np_utils from sklearn.cluster import KMeans import matplotlib.pyplot as plt -from verve.data_generation import dataGen +from libra.data_generation import dataGen from keras.models import Sequential from keras.layers import (Dense, Conv2D, Flatten) from os import listdir -from verve.modeling.tuner import tuneReg, tuneClass, tuneCNN +from libra.modeling.tuner import tuneReg, tuneClass, tuneCNN from sklearn.neighbors import KNeighborsClassifier from sklearn.ensemble import RandomForestRegressor from sklearn.feature_selection import SelectFromModel diff --git a/verve/modeling/__init__.py b/libra/modeling/__init__.py similarity index 100% rename from verve/modeling/__init__.py rename to libra/modeling/__init__.py diff --git a/verve/modeling/prediction_model_creation.py b/libra/modeling/prediction_model_creation.py similarity index 100% rename from verve/modeling/prediction_model_creation.py rename to libra/modeling/prediction_model_creation.py diff --git a/verve/modeling/tuner.py b/libra/modeling/tuner.py similarity index 100% rename from verve/modeling/tuner.py rename to libra/modeling/tuner.py diff --git a/verve/plotting/__init__.py b/libra/plotting/__init__.py similarity index 100% rename from verve/plotting/__init__.py rename to libra/plotting/__init__.py diff --git a/verve/plotting/generate_plots.py b/libra/plotting/generate_plots.py similarity index 100% rename from verve/plotting/generate_plots.py rename to libra/plotting/generate_plots.py diff --git a/verve/plotting/nonkeras_generate_plots.py b/libra/plotting/nonkeras_generate_plots.py similarity index 100% rename from verve/plotting/nonkeras_generate_plots.py rename to libra/plotting/nonkeras_generate_plots.py diff --git a/verve/preprocessing/NLP_preprocessing.py b/libra/preprocessing/NLP_preprocessing.py similarity index 97% rename from verve/preprocessing/NLP_preprocessing.py rename to libra/preprocessing/NLP_preprocessing.py index 1cb5e57d..d684052f 100644 --- a/verve/preprocessing/NLP_preprocessing.py +++ b/libra/preprocessing/NLP_preprocessing.py @@ -4,8 +4,8 @@ import spacy from spacy.lang.en import English from nltk.corpus import stopwords -from verve.data_generation.dataset_labelmatcher import get_similar_column -from verve.data_generation.grammartree import get_value_instruction +from libra.data_generation.dataset_labelmatcher import get_similar_column +from libra.data_generation.grammartree import get_value_instruction def get_target_values(data, instruction, yLabel): diff --git a/verve/preprocessing/__init__.py b/libra/preprocessing/__init__.py similarity index 100% rename from verve/preprocessing/__init__.py rename to libra/preprocessing/__init__.py diff --git a/verve/preprocessing/data_preprocesser.py b/libra/preprocessing/data_preprocesser.py similarity index 98% rename from verve/preprocessing/data_preprocesser.py rename to libra/preprocessing/data_preprocesser.py index b77569cd..c9347c72 100644 --- a/verve/preprocessing/data_preprocesser.py +++ b/libra/preprocessing/data_preprocesser.py @@ -8,8 +8,8 @@ from sklearn.preprocessing import (OneHotEncoder, StandardScaler, FunctionTransformer) -from verve.data_generation.dataset_labelmatcher import get_similar_column -from verve.data_generation.grammartree import get_value_instruction +from libra.data_generation.dataset_labelmatcher import get_similar_column +from libra.data_generation.grammartree import get_value_instruction from prince.ca import CA from nltk.stem import WordNetLemmatizer diff --git a/verve/preprocessing/data_reader.py b/libra/preprocessing/data_reader.py similarity index 100% rename from verve/preprocessing/data_reader.py rename to libra/preprocessing/data_reader.py diff --git a/verve/preprocessing/huggingface_model_finetune_helper.py b/libra/preprocessing/huggingface_model_finetune_helper.py similarity index 100% rename from verve/preprocessing/huggingface_model_finetune_helper.py rename to libra/preprocessing/huggingface_model_finetune_helper.py diff --git a/verve/preprocessing/image_caption_helpers.py b/libra/preprocessing/image_caption_helpers.py similarity index 100% rename from verve/preprocessing/image_caption_helpers.py rename to libra/preprocessing/image_caption_helpers.py diff --git a/verve/preprocessing/image_preprocesser.py b/libra/preprocessing/image_preprocesser.py similarity index 99% rename from verve/preprocessing/image_preprocesser.py rename to libra/preprocessing/image_preprocesser.py index 835ab8ce..ae482bf6 100644 --- a/verve/preprocessing/image_preprocesser.py +++ b/libra/preprocessing/image_preprocesser.py @@ -3,8 +3,8 @@ import cv2 import glob import pandas as pd -from verve.data_generation.dataset_labelmatcher import get_similar_column -from verve.data_generation.grammartree import get_value_instruction +from libra.data_generation.dataset_labelmatcher import get_similar_column +from libra.data_generation.grammartree import get_value_instruction # Preprocesses images from images to median of heighs/widths diff --git a/verve/queries.py b/libra/queries.py similarity index 98% rename from verve/queries.py rename to libra/queries.py index fca3a12d..219b9771 100644 --- a/verve/queries.py +++ b/libra/queries.py @@ -1,20 +1,20 @@ -from verve.query.nlp_queries import (image_caption_query, +from libra.query.nlp_queries import (image_caption_query, generate_caption, classify_text, text_classification_query, get_summary, summarization_query) -from verve.query.classification_models import (k_means_clustering, +from libra.query.classification_models import (k_means_clustering, train_svm, nearest_neighbors, decision_tree) -from verve.query.supplementaries import tune_helper, get_model_data, get_operators, get_accuracy, get_losses, get_target, get_plots, get_vocab +from libra.query.supplementaries import tune_helper, get_model_data, get_operators, get_accuracy, get_losses, get_target, get_plots, get_vocab -from verve.query.feedforward_nn import (regression_ann, +from libra.query.feedforward_nn import (regression_ann, classification_ann, convolutional) -from verve.data_generation.grammartree import get_value_instruction -from verve.data_generation.dataset_labelmatcher import (get_similar_column, +from libra.data_generation.grammartree import get_value_instruction +from libra.data_generation.dataset_labelmatcher import (get_similar_column, get_similar_model) -from verve.plotting.generate_plots import analyze +from libra.plotting.generate_plots import analyze from colorama import Fore, Style import pandas as pd from pandas.core.common import SettingWithCopyWarning @@ -829,4 +829,4 @@ def analyze(self, model=None): if model is None: model = self.latest_model clearLog() - analyze(self, model) \ No newline at end of file + analyze(self, model) diff --git a/verve/query/__init__.py b/libra/query/__init__.py similarity index 100% rename from verve/query/__init__.py rename to libra/query/__init__.py diff --git a/verve/query/classification_models.py b/libra/query/classification_models.py similarity index 98% rename from verve/query/classification_models.py rename to libra/query/classification_models.py index 951759df..8e6cd14a 100644 --- a/verve/query/classification_models.py +++ b/libra/query/classification_models.py @@ -1,14 +1,14 @@ import pandas as pd -from verve.preprocessing.data_reader import DataReader +from libra.preprocessing.data_reader import DataReader from sklearn import svm, tree from sklearn.metrics import accuracy_score -from verve.preprocessing.data_preprocesser import initial_preprocesser, clustering_preprocessor +from libra.preprocessing.data_preprocesser import initial_preprocesser, clustering_preprocessor from sklearn.cluster import KMeans from sklearn.neighbors import KNeighborsClassifier from sklearn.model_selection import cross_val_score import numpy as np -from verve.query.supplementaries import generate_id -from verve.plotting.generate_plots import (generate_clustering_plots) +from libra.query.supplementaries import generate_id +from libra.plotting.generate_plots import (generate_clustering_plots) from colorama import Fore, Style import warnings diff --git a/verve/query/dimensionality_red_queries.py b/libra/query/dimensionality_red_queries.py similarity index 98% rename from verve/query/dimensionality_red_queries.py rename to libra/query/dimensionality_red_queries.py index 3b052400..3e955bd2 100644 --- a/verve/query/dimensionality_red_queries.py +++ b/libra/query/dimensionality_red_queries.py @@ -1,12 +1,12 @@ from sklearn.feature_selection import SelectFromModel from sklearn import preprocessing, tree from itertools import product, permutations -from verve.preprocessing.data_reader import DataReader +from libra.preprocessing.data_reader import DataReader import os from sklearn.ensemble import RandomForestRegressor -from verve.preprocessing.data_preprocesser import structured_preprocesser, initial_preprocesser -from verve.data_generation.grammartree import get_value_instruction -from verve.data_generation.dataset_labelmatcher import get_similar_column +from libra.preprocessing.data_preprocesser import structured_preprocesser, initial_preprocesser +from libra.data_generation.grammartree import get_value_instruction +from libra.data_generation.dataset_labelmatcher import get_similar_column from xgboost import XGBClassifier diff --git a/verve/query/feedforward_nn.py b/libra/query/feedforward_nn.py similarity index 98% rename from verve/query/feedforward_nn.py rename to libra/query/feedforward_nn.py index c0ab931e..0c6c6cc7 100644 --- a/verve/query/feedforward_nn.py +++ b/libra/query/feedforward_nn.py @@ -1,22 +1,22 @@ from colorama import Fore, Style from tensorflow.keras.callbacks import EarlyStopping import os -from verve.preprocessing.image_preprocesser import (setwise_preprocessing, +from libra.preprocessing.image_preprocesser import (setwise_preprocessing, csv_preprocessing, classwise_preprocessing, set_distinguisher, already_processed) -from verve.preprocessing.data_reader import DataReader +from libra.preprocessing.data_reader import DataReader from keras.models import Sequential from keras.layers import (Dense, Conv2D, Flatten, MaxPooling2D, ) import pandas as pd -from verve.query.supplementaries import save, generate_id +from libra.query.supplementaries import save, generate_id from keras.preprocessing.image import ImageDataGenerator from sklearn.preprocessing import OneHotEncoder -from verve.plotting.generate_plots import (generate_regression_plots, +from libra.plotting.generate_plots import (generate_regression_plots, generate_classification_plots) -from verve.preprocessing.data_preprocesser import initial_preprocesser -from verve.modeling.prediction_model_creation import get_keras_model_reg, get_keras_model_class +from libra.preprocessing.data_preprocesser import initial_preprocesser +from libra.modeling.prediction_model_creation import get_keras_model_reg, get_keras_model_class from sklearn.preprocessing import StandardScaler import numpy as np diff --git a/verve/query/nlp_queries.py b/libra/query/nlp_queries.py similarity index 97% rename from verve/query/nlp_queries.py rename to libra/query/nlp_queries.py index a41c4401..b036a77f 100644 --- a/verve/query/nlp_queries.py +++ b/libra/query/nlp_queries.py @@ -11,19 +11,19 @@ from transformers import T5Tokenizer, T5ForConditionalGeneration import tensorflow as tf -from verve.data_generation.dataset_labelmatcher import get_similar_column -from verve.data_generation.grammartree import get_value_instruction -from verve.modeling.prediction_model_creation import get_keras_text_class -import verve.plotting.nonkeras_generate_plots -from verve.plotting.generate_plots import generate_classification_plots -from verve.preprocessing.NLP_preprocessing import get_target_values, text_clean_up, lemmatize_text, encode_text -from verve.preprocessing.huggingface_model_finetune_helper import CustomDataset, train, inference -from verve.preprocessing.image_caption_helpers import load_image, map_func, CNN_Encoder, RNN_Decoder, get_path_column, \ +from libra.data_generation.dataset_labelmatcher import get_similar_column +from libra.data_generation.grammartree import get_value_instruction +from libra.modeling.prediction_model_creation import get_keras_text_class +import libra.plotting.nonkeras_generate_plots +from libra.plotting.generate_plots import generate_classification_plots +from libra.preprocessing.NLP_preprocessing import get_target_values, text_clean_up, lemmatize_text, encode_text +from libra.preprocessing.huggingface_model_finetune_helper import CustomDataset, train, inference +from libra.preprocessing.image_caption_helpers import load_image, map_func, CNN_Encoder, RNN_Decoder, get_path_column, \ generate_caption_helper # Sentiment analysis predict wrapper -from verve.query.supplementaries import save -from verve.plotting.generate_plots import plot_loss +from libra.query.supplementaries import save +from libra.plotting.generate_plots import plot_loss counter = 0 @@ -374,7 +374,7 @@ def summarization_query(self, instruction, preprocess=True, plots = {} if generate_plots: logger("Generating plots") - plots.update({"loss": verve.plotting.nonkeras_generate_plots.plot_loss(total_loss_train, total_loss_val)}) + plots.update({"loss": libra.plotting.nonkeras_generate_plots.plot_loss(total_loss_train, total_loss_val)}) if save_model: logger("Saving model") @@ -687,7 +687,7 @@ def val_step(img_tensor, target): plots = {} if generate_plots: logger("Generating plots") - plots.update({"loss": verve.plotting.nonkeras_generate_plots.plot_loss(loss_plot_train, loss_plot_val)}) + plots.update({"loss": libra.plotting.nonkeras_generate_plots.plot_loss(loss_plot_train, loss_plot_val)}) logger("->", "Final training loss: {}".format(str(total_loss.numpy() / num_steps))) total_loss = total_loss.numpy() / num_steps diff --git a/verve/query/supplementaries.py b/libra/query/supplementaries.py similarity index 99% rename from verve/query/supplementaries.py rename to libra/query/supplementaries.py index af4e4f39..1285e54f 100644 --- a/verve/query/supplementaries.py +++ b/libra/query/supplementaries.py @@ -1,12 +1,12 @@ import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' -from verve.modeling.tuner import (tuneReg, +from libra.modeling.tuner import (tuneReg, tuneClass, tuneCNN) import matplotlib.pyplot as plt -from verve.preprocessing.data_reader import DataReader +from libra.preprocessing.data_reader import DataReader from keras.preprocessing.image import ImageDataGenerator -from verve.plotting.generate_plots import (generate_regression_plots, +from libra.plotting.generate_plots import (generate_regression_plots, generate_classification_plots) import uuid diff --git a/verve/query/unused_functions.py b/libra/query/unused_functions.py similarity index 100% rename from verve/query/unused_functions.py rename to libra/query/unused_functions.py diff --git a/setup.py b/setup.py index f2053db8..d11ac820 100644 --- a/setup.py +++ b/setup.py @@ -4,14 +4,14 @@ long_description = fh.read() setuptools.setup( - name="verve", # Replace with your own username + name="libra", # Replace with your own username version="0.0.1", author="Palash Shah", author_email="ps9cmk@virginia.edu", description="Fully automated machine learning in one-liners.", long_description=long_description, long_description_content_type="text/markdown", - url="https://github.com/Palashio/verve", + url="https://github.com/Palashio/libra", packages=setuptools.find_packages(), classifiers=[ "Programming Language :: Python :: 3", diff --git a/site/html/about.html b/site/html/about.html index abefc32e..ee81d933 100644 --- a/site/html/about.html +++ b/site/html/about.html @@ -3,7 +3,7 @@ - + @@ -11,13 +11,13 @@ @@ -44,9 +44,9 @@
  • Automation
    • -

    verve APIs can handle multiple tasks required for machine learning with less code, allowing for easier debugging and better readability

    -

    Being open source means that verve is free! You can still help us by providing feedback for future releases

    -

    Join the community of developers who are passionate about machine learning and help contribute to verve APIs

    +

    libra APIs can handle multiple tasks required for machine learning with less code, allowing for easier debugging and better readability

    +

    Being open source means that libra is free! You can still help us by providing feedback for future releases

    +

    Join the community of developers who are passionate about machine learning and help contribute to libra APIs

    Easy data preprocessing and data input types allow for larger and more complicated projects

    Collaborate with others by using open source api calls that are updated frequently to suit more needs

    Focus on your machine learning ideas and not the tedious work of building models from the ground up

    diff --git a/site/html/dataset.html b/site/html/dataset.html index bf70092a..e23e524a 100644 --- a/site/html/dataset.html +++ b/site/html/dataset.html @@ -1,26 +1,26 @@ - About verve + About libra - +
    - +