Here is my data,
my_df <- data.frame(some_data = sample(1:100, 21, replace=T))
I would like to use this time series with ARIMA from fpp package and state that frequency/period is 3. Previously, with forecast package it was easy, I could you use ts(., frequency = 3), but I am not sure how to do it with fpp.
The fpp package simply contains data not functions. It depends on the forecast package for all modelling. So you can use ts(my_df$some_data, frequency=3) as usual.
Related
I am doing a college project where I need to compare a string with list of other strings. I want to know if we have any kind of library which can do this or not.
Suppose I have a table called : DOCTORS_DETAILS
Other Table names are : HOSPITAL_DEPARTMENTS , DOCTOR_APPOINTMENTS, PATIENT_DETAILS,PAYMENTS etc.
Now I want to calculate which one among those are more relevant to DOCTOR_DETAILS ?
Expected output can be,
DOCTOR_APPOINTMENTS - More relevant because of the term doctor matches in both string
PATIENT_DETAILS - The term DETAILS present in both string
HOSPITAL_DEPARTMENTS - least relevant
PAYMENTS - least relevant
Therefore I want to find RELEVENCE based on number of similar terms present on both the strings in question.
Ex : DOCTOR_DETAILS -> DOCTOR_APPOITMENT(1/2) > DOCTOR_ADDRESS_INFORMATION(1/3) > DOCTOR_SPECILIZATION_DEGREE_INFORMATION (1/4) > PATIENT_INFO (0/2)
Semantic similarity is a common NLP problem. There are multiple approaches to look into, but at their core they all are going to boil down to:
Turn each piece of text into a vector
Measure distance between vectors, and call closer vectors more similar
Three possible ways to do step 1 are:
tf-idf
fasttext
bert-as-service
To do step 2, you almost certainly want to use cosine distance. It is pretty straightforward with Python, here is a implementation from a blog post:
import numpy as np
def cos_sim(a, b):
"""Takes 2 vectors a, b and returns the cosine similarity according
to the definition of the dot product
"""
dot_product = np.dot(a, b)
norm_a = np.linalg.norm(a)
norm_b = np.linalg.norm(b)
return dot_product / (norm_a * norm_b)
For your particular use case, my instincts say to use fasttext. So, the official site shows how to download some pretrained word vectors, but you will want to download a pretrained model (see this GH issue, use https://dl.fbaipublicfiles.com/fasttext/vectors-english/wiki-news-300d-1M-subword.bin.zip),
Then you'd then want to do something like:
import fasttext
model = fasttext.load_model("model_filename.bin")
def order_tables_by_name_similarity(main_table, candidate_tables):
'''Note: we use a fasttext model, not just pretrained vectors, so we get subword information
you can modify this to also output the distances if you need them
'''
main_v = model[main_table]
similarity_to_main = lambda w: cos_sim(main_v, model[w])
return sorted(candidate_tables, key=similarity_to_main, reverse=True)
order_tables_by_name_similarity("DOCTORS_DETAILS", ["HOSPITAL_DEPARTMENTS", "DOCTOR_APPOINTMENTS", "PATIENT_DETAILS", "PAYMENTS"])
# outputs: ['PATIENT_DETAILS', 'DOCTOR_APPOINTMENTS', 'HOSPITAL_DEPARTMENTS', 'PAYMENTS']
If you need to put this in production, the giant model size (6.7GB) might be an issue. At that point, you'd want to build your own model, and constrain the model size. You can probably get roughly the same accuracy out of a 6MB model!
I have a ML.net project and as of right now everything has gone great. I have a motor that collects a power reading 256 times around each rotation and I push that into a model. Right now it determines the state of the motor nearly perfectly. The motor itself only has room for 38 values on it at a time so I have been spending several rotations to collect the full 256 samples for my training data.
I would like to cut the sample size down to 38 so every rotation I can determine its state. If I just evenly space the samples down to 38 my model degrades by a lot. I know I am not feeding the model the features it thinks are most important but just making a guess and randomly selecting data for the model.
Is there a way I can see the importance of each value in the array during the training process? I was thinking I could use IDataView for this and I found the below statement about it (link).
Standard ML schema: The IDataView system does not define, nor prescribe, standard ML schema representation. For example, it does not dictate representation of nor distinction between different semantic interpretations of columns, such as label, feature, score, weight, etc. However, the column metadata support, together with conventions, may be used to represent such interpretations.
Does this mean I can print out such things as weight for each column and how would I do that?
I have actually only been working with ML.net for a couple weeks now so I apologize if the question is naive, I assure you I have googled this as many ways as I can think to. Any advice would be appreciated. Thanks in advance.
EDIT:
Thank you for the answer I was going down a completely useless path. I have been trying to get it to work following the example you linked to. I have 260 columns with numbers and one column with the conditions as one of five text strings. This is the condition I am trying to predict.
The first time I tried it threw an error "expecting single but got string". No problem I used .Append(mlContext.Transforms.Conversion.MapValueToKey("Label", "Label")) to convert to key values and it threw the error expected Single, got Key UInt32. any ideas on how to push that into this function?
At any rate thank you for the reply but I guess my upvotes don't count yet sorry. hopefully I can upvote it later or someone else here can upvote it. Below is the code example.
//Create MLContext
MLContext mlContext = new MLContext();
//Load Data
IDataView data = mlContext.Data.LoadFromTextFile<ModelInput>(TRAIN_DATA_FILEPATH, separatorChar: ',', hasHeader: true);
// 1. Get the column name of input features.
string[] featureColumnNames =
data.Schema
.Select(column => column.Name)
.Where(columnName => columnName != "Label").ToArray();
// 2. Define estimator with data pre-processing steps
IEstimator<ITransformer> dataPrepEstimator =
mlContext.Transforms.Concatenate("Features", featureColumnNames)
.Append(mlContext.Transforms.NormalizeMinMax("Features"))
.Append(mlContext.Transforms.Conversion.MapValueToKey("Label", "Label"));
// 3. Create transformer using the data pre-processing estimator
ITransformer dataPrepTransformer = dataPrepEstimator.Fit(data);//error here
// 4. Pre-process the training data
IDataView preprocessedTrainData = dataPrepTransformer.Transform(data);
// 5. Define Stochastic Dual Coordinate Ascent machine learning estimator
var sdcaEstimator = mlContext.Regression.Trainers.Sdca();
// 6. Train machine learning model
var sdcaModel = sdcaEstimator.Fit(preprocessedTrainData);
ImmutableArray<RegressionMetricsStatistics> permutationFeatureImportance =
mlContext
.Regression
.PermutationFeatureImportance(sdcaModel, preprocessedTrainData, permutationCount: 3);
// Order features by importance
var featureImportanceMetrics =
permutationFeatureImportance
.Select((metric, index) => new { index, metric.RSquared })
.OrderByDescending(myFeatures => Math.Abs(myFeatures.RSquared.Mean));
Console.WriteLine("Feature\tPFI");
foreach (var feature in featureImportanceMetrics)
{
Console.WriteLine($"{featureColumnNames[feature.index],-20}|\t{feature.RSquared.Mean:F6}");
}
I believe what you are looking for is called Permutation Feature Importance. This will tell you which features are most important by changing each feature in isolation, and then measuring how much that change affected the model's performance metrics. You can use this to see which features are the most important to the model.
Interpret model predictions using Permutation Feature Importance is the doc that describes how to use this API in ML.NET.
You can also use an open-source set of packages, they are much more sophisticated than what is found in ML.NET. I have an example on my GitHub how-to use R with advanced explainer packages to explain ML.NET models. You can get local instance as well as global model breakdown/details/diagnostics/feature interactions etc.
https://github.com/bartczernicki/BaseballHOFPredictionWithMlrAndDALEX
I have some tabular device data comprising a
time column, some tabular features, target classes
There are around 500 rows (not same) in all devices data and target classes are same.
I have same data for around 1000 devices,
I want to train a general model for all the devices for detecting the class.
Can someone help me with the approach to train for the target variable. What kind of models work in this condition
If your device type is part of the data, you can train a decision tree. If the device type feature is important for classification sake, it will be added to the tree. First, create the device type features yourself - a binary column for each device type, like done in one-hot encoding. There will be a binary column per device type - is_device_samsung, is_device_lg, is_device_iphone and so forth. The number of columns created is equal to the number of device types. All but one of these columns will be 0, and the one indicating the current type will be 1. This will not guarantee the device type will be a part of the model - but let the AI decide this for you.
BTW - don't use get_dummies unless you know how to reuse it exactly as needed in the test data.
Another option is to use the python-weka wrapper, which accepts nominal attributes:
Example:
import weka.core.jvm as jvm
from weka.core.converters import Loader
from weka.classifiers import Classifier
def get_weka_prob(inst):
dist = c.distribution_for_instance(inst)
p = dist[next((i for i, x in enumerate(inst.class_attribute.values) if x == 'DONE'), -1)]
return p
jvm.start()
loader = Loader(classname="weka.core.converters.CSVLoader")
data = loader.load_file(r'.\recs_csv\df.csv')
data.class_is_last()
datatst = loader.load_file(r'.\recs_csv\dftst.csv')
datatst.class_is_last()
c = Classifier("weka.classifiers.trees.J48", options=["-C", "0.1"])
c.build_classifier(data)
print(c)
probstst = [get_weka_prob(inst) for inst in datatst]
jvm.stop()
Weka models are different models that use a java bridge to python - the methods are java methods that can be called using this bridge. To use the dataframe in sklearn - you would have to manipulate it with one-hot encoding. Note that the nominal attributes in weka cannot have any special character in them. so use
df = df.replace([',', '"', "'", "%", ";"], '', regex=True)
for any nominal attribute before saving it to csv.
If you want to ensure that the model_type feature will be included in your model, you can trick it and add a dummy model type - and ensure that the class column for this dummy model is always "1" or "True" - depending on your class variable. If you have enough rows with this dummy model - j48 will open it as the first branch. Once the attribute is selected by j48 - it will be branched for all of the model types, not just the dummy one.
I have built an Microsoft Azure ML Studio workspace predictive web service, and have a scernario where I need to be able to run the service with different training datasets.
I know I can setup multiple web services via Azure ML, each with a different training set attached, but I am trying to find a way to do it all within the same workspace and passing a Web Input Parameter as the input value to choose which training set to use.
I have found this article, which describes almost my scenario. However, this article relies on the training dataset that is being pulled from the Load Trained Data module, as having a static endpoint (or blob storage location). I don't see any way to dynamically (or conditionally) change this location based on a Web Input Parameter.
Basically, does Azure ML support a "conditional training data" loading?
Or, might there be a way to combine training datasets, then filter based on the passed Web Input Parameter?
This probably isn't exactly what you need, but hopefully, it helps you out.
To combine data sets, you can use the Join Data module.
To filter, that may be accomplished by executing a Python script. Here's an example.
Using the Adult Census Income Binary Classification dataset, on the age column, there's a minimum age of 17.
If I wanted to filter the data set by age, connect it to an Execute Python Script module and here's the filtering code with the pandas query method.
# The script MUST contain a function named azureml_main
# which is the entry point for this module.
import pandas as pd
def azureml_main(dataframe1 = None, dataframe2 = None):
# Return value must be of a sequence of pandas.DataFrame
return dataframe1.query("age >= 25")
And looking at that output it filters out the data set where the minimum age is now 25.
Sure, you can do that. What you would want is to use an Execute R Script or SQL Transformation module to determine, based on your input data, what model to use. Something like this:
Notice, your input data is cleaned/updated/feature engineered, then it's passed to two different SQL transforms which will tell it to go to one of two paths.
Each path has it's own training data.
Note: I am not exactly sure what your use case is, but if it were me, I would instead train two different models using the two different training data, then try to just use the models in my web service, not actually train on the web service as that would likely be quite slow.
I am currently experimenting with distributed tensorflow.
I am using the tf.estimator.Estimator class (custom model function) together with tf.contrib.learn.Experiment and managed it to get a working data parallel execution.
However, I would now like to try model parallel execution. I was not able to find any example for that, except Implementation of model parallelism in tensorflow.
But I am not sure how to implement this using tf.estimators (e.g. how to deal with the input functions?).
Does anybody have any experience with it or can provide a working example?
First up, you should stop using tf.contrib.learn.Estimator in favor of tf.estimator.Estimator, because contrib is an experimental module, and classes that have graduated to the core API (such es Estimator) automatically get deprecated.
Now, back to your main question, you can create a distributed model and pass it via model_fn parameter of tf.estimator.Estimator.__init__.
def my_model(features, labels, mode):
net = features[X_FEATURE]
with tf.device('/device:GPU:1'):
for units in [10, 20, 10]:
net = tf.layers.dense(net, units=units, activation=tf.nn.relu)
net = tf.layers.dropout(net, rate=0.1)
with tf.device('/device:GPU:2'):
logits = tf.layers.dense(net, 3, activation=None)
onehot_labels = tf.one_hot(labels, 3, 1, 0)
loss = tf.losses.softmax_cross_entropy(onehot_labels=onehot_labels,
logits=logits)
optimizer = tf.train.AdagradOptimizer(learning_rate=0.1)
train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
[...]
classifier = tf.estimator.Estimator(model_fn=my_model)
The model above defines 6 layers with /device:GPU:1 placement and 3 other layers with /device:GPU:2 placement. The return value of my_model function should be an EstimatorSpec instance. A complete working example can be found in tensorflow examples.